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Software Development
Simplifying Enterprise Cloud Migration: A Winning Strategy for Your Business
5 min read

Enterprise cloud migration brings innovation to IT operations and takes enterprises one step closer to agility. After all, it’s all about streamlining processes, increasing efficiency, and reducing costs. But if not done right, cloud migration can prove to be a nightmare for enterprises. They have to move a large amount of data without which the organization cannot function properly.

If you are stuck with the intricate dilemma of cloud migration, be ready to decode the myths. This article is all about decrypting and implementing enterprise cloud migration. You’ll be learning the best practices to migrate your existing data from your previous infrastructure to the cloud.

What is cloud Migration?

Cloud migration is the practice of transferring your data, digital assets, and other IT resources into the cloud. Enterprises move their databases and applications from physical infrastructure or one cloud to another partially or completely.

Understanding Enterprise Cloud Migration

In enterprise cloud migration, the cloud refers to remote servers and data centers. These are usually hosted and managed by third parties that offer a range of services and resources over the Internet. This allows businesses to access and utilize computing resources on demand as per their needs.

Statistics Highlighting the Growth and Adoption of Enterprise Cloud Migration

The migration to the cloud has gained significant momentum over the past few years. According to Google, 41.4% of global technopreneurs are eager to invest in cloud-based services in 2023. The study also shows that almost half are either training existing staff or hiring new resources to improve cloud efficiency.

Recent research by Mckinsey states that cloud technology has crossed the $1 trillion dollar market value as an industry. What’s more astonishing is the fact that by 2024, out of every $10 spent on IT hosting, $8 will be on the cloud.

it-hosting spend to the cloud

Organizations that have already adopted enterprise cloud migration are reaping its benefits.

As per Salesforce, 69% of companies are spending on this technology. And 94% of businesses saw an improvement in security after migrating to the cloud.

A Google Cloud survey has shown that 54% of businesses are into enterprise cloud migration mainly to reduce costs and increase revenues. 

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Gartner’s research claims that 50% of enterprises will move towards the cloud by 2027 to achieve agility. This supports Forbes’ report which has previously claimed that 83% of organizations are expected to shift their workload to the cloud.

percent of workloads run/will run from the following

These statistics underscore the growing confidence and success organizations experience through cloud migration.

Key Models and Strategies for Enterprise Cloud Migration

When considering cloud migration, enterprises can adopt various models and strategies. Of course, the choice depends on their unique requirements and objectives. These are six different types of enterprise cloud migration models that are in practice today.

6 types of clouds migration

Lift and Shift Model or Rehosting

This cloud migration model is about lifting the database or stack from on-premises hosting and shifting it to the cloud. In doing so, an exact copy of your existing environment is transported without making changes. Rehosting is suitable for enterprises that don’t have a long-term strategy or possess a conservative culture.

Lift and Optimize or Replatforming

This is a variation of rehosting that involves a few adjustments in the cloud landscape for optimization. However, the core architecture in the re-platforming model stays the same. This is also suitable for conservative enterprises who are looking for speed and agility.

Drop and Shop or Repurchasing

Repurchasing the enterprise cloud migration model is about transferring applications to a new, cloud-native product. However, there’s always a challenge of training the team and fear of losing the familiarity of existing code. If you are moving data from a customized landscape, drop and shop model is the ideal pick for you.

Re-Architect or Refactoring

Refactoring refers to rebuilding your applications on the cloud from scratch. It’s an expensive choice but suitable for your future needs. It helps you leverage cloud capabilities that were absent in previous environments. For example, you can go for serverless computing or cloud auto-scaling.


In some cases turning application is the wise and suitable choice. This is useful if you feel some applications are no longer required after assessing their portfolio for cloud readiness. Retiring can help you boost the process and make cloud migration easier.

Hybrid Model or Retaining

This is an ideal model for enterprises for whom cloud migration doesn’t make sense. Some might have recently updated their applications while others are unable to move for compliance reasons. If you are such an enterprise, only make the decision after contacting experts.

Benefits and Challenges Associated with Enterprise Cloud Migration

As with any major IT initiative, enterprise cloud migration comes with its share of benefits and challenges:

Benefits of Enterprise Cloud Computing

Benefits of Enterprise Cloud Computing

Cost Savings: By transitioning from capital-intensive on-premises infrastructure to pay-as-you-go cloud services, organizations can reduce IT costs significantly.

Scalability and Flexibility: Cloud services allow businesses to scale resources up or down as needed. This provides them with the agility to meet changing demands of their business landscape.

Enhanced Security: Leading cloud providers invest in robust security measures. These are security measures that often surpass what organizations can achieve by themselves on-premises.

Innovation Acceleration: Cloud-native technologies and services enable organizations to innovate rapidly and deploy new features quickly.

Challenges of Cloud Migration

Data Migration Complexity: Transferring large volumes of data to the cloud can be time-consuming and challenging. This is especially challenging if you are migrating data with limited bandwidth.

Application Compatibility: Some legacy applications may require modification to work effectively in the cloud environment.

Security and Compliance Concerns: Ensuring data security and regulatory compliance during and after migration is crucial.

Vendor Lock-In: Moving between cloud providers can be complex, potentially leading to vendor lock-in.

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Best Practices for Successful Enterprise Cloud Migration

A successful enterprise cloud migration requires meticulous planning, thoughtful decision-making, and effective execution. Let's explore some best practices to ensure a smooth and successful migration process.

Pre-migration Planning and Assessment

Before embarking on the cloud migration journey, organizations must undertake a comprehensive planning and assessment process:

Pre-migration Planning and Assessment

Importance of Cloud Migration Checklist

Creating a detailed checklist of migration tasks and requirements helps organizations stay organized. This is the practice that ensures no critical aspects are overlooked during the process. The checklist should cover areas such as application dependencies, data backup, security measures, and post-migration testing.

Evaluating Existing Infrastructure and Applications

A thorough assessment of existing infrastructure and applications is essential to determine their suitability for the cloud environment. This assessment helps identify potential roadblocks and provides insights into which applications can be migrated. Furthermore, you can know which applications require adjustments for migration.

Considerations for Data Migration and Security

For data migration, organizations have to establish robust strategies for data integrity, privacy, and compliance. Implementing proper encryption and access controls is vital to safeguard sensitive data during transit and storage.

Selecting the Right Cloud Provider and Model

Choosing the most suitable cloud service provider and deployment model is pivotal for the success of cloud migration:

Evaluating Different Cloud Service Providers

The choice of cloud provider impacts various aspects of migration, including performance, service offerings, pricing, and regional availability. Organizations should conduct a thorough evaluation of major cloud providers like AWS, Azure, Google Cloud, and others, considering their specific needs.

According to Statista, Cloud services generated $63 billion dollars in revenue in the first quarter of 2023. Amazon AWS, Microsoft Azure, and Google Cloud have 32%, 23%, and 10% shares respectively in this.

Evaluating Different Cloud Service Providers

It’s hard to calculate the exact cost of service providers as it depends on what you are getting. But the big three (AWS, Azure, and Google Cloud) as well as IBM offer various discounts to its users. By leveraging these discounts, you can greatly reduce the overall cost of your cloud hosting.

cloud service providers

Choosing Between Public, Private, or Hybrid Cloud Models

Your decision to opt for public, private, or hybrid cloud models depends on various factors. These include but aren’t limited to data sensitivity, regulatory requirements, and budget constraints. A hybrid cloud approach, which combines public and private clouds, offers flexibility in managing sensitive data. It also helps in leveraging the scalability of public cloud services.

Aligning Cloud Strategy with Business Goals

The cloud migration strategy should align with the organization's broader business objectives. Whether it's achieving cost savings, improving performance, or enhancing customer experience, the cloud migration approach should support these goals.

Implementing the Migration Process

With a robust plan in place, it's time to execute the migration process:

Implementing the Migration Process

Creating a Phased Approach

Adopting a phased approach is often more manageable and minimizes the risk of disruption. Migrating applications and data in batches allows the IT team to address any challenges that arise in a controlled manner.

Prioritizing Critical Applications and Data

Not all applications and data have the same level of urgency for migration. Prioritizing critical business applications and data ensures that essential functions continue smoothly during the migration process.

Ensuring Minimal Disruption to Operations

You should try to minimize any disruption to ongoing business operations during the migration process. So, ensure proper communication and collaboration among stakeholders, IT teams, and end-users. This plays a crucial role in ensuring a seamless transition.

Post-migration Optimization and Evaluation

Once migration is complete, the focus shifts to optimizing and evaluating the newly established cloud infrastructure:

Post-migration Optimization and Evaluation

Monitoring and Optimizing Performance

Continuous monitoring of cloud infrastructure performance is essential to identify and address any bottlenecks or inefficiencies. Cloud-native monitoring tools can provide real-time insights into resource utilization and application performance.

Assessing the Success through Key Performance Indicators (KPIs)

Defining key performance indicators (KPIs) for cloud migration success is vital to measure the project's outcomes. KPIs may include:

  • Cost savings
  • Uptime
  • Customer satisfaction
  • Application response times

Addressing Any Challenges or Failures 

Migrating to the cloud is a complex process, and challenges or failures may arise. A post-mortem analysis of any issues encountered can provide valuable insights for future improvements and projects. Some common challenges that data scientists often have to face include:

  • Incomplete inventory (artifacts and respective ETLs)
  • Limited documentation of current systems
  • Data Redundancy
  • Complex workflow, job flow, and data flow.
  • Multiple processing (Scripts, View, SP, ETL, etc.)
data migration challenges and solution for successful implementation

How shopdev Can Help to Migrate

Shopdev has been helping enterprises with cloud migration for over half a decade now. We provide tailored cloud migration solutions to businesses with a team of seasoned cloud experts. With our proven track record, we bring a wealth of experience to ensure the success of every cloud migration project.

Tailored Solutions for Enterprise Cloud Migration

Shopdev offers a range of cloud migration services to enterprises looking to migrate to the cloud. We are a proud partner of all the big companies such as Amazon AWS, Azure, Google Cloud, and more.

Tailored Solutions for Enterprise Cloud Migration

We conduct an in-depth assessments of your organization's existing infrastructure and applications to gauge their readiness for migration. Based on the assessment, we craft a detailed migration plan. This helps us address potential challenges and in optimizing the migration process.

The Shopdev team works closely with you to identify the most suitable cloud provider and deployment model. We consider your organization's unique requirements while offering suggestions and recommendations. This collaborative approach ensures alignment with the client's goals and budget.

In the end, our experts execute the migration process in a phased manner, ensuring minimal disruption to business operations. In the post-migration stage, we provide ongoing support to address issues and optimize the cloud infrastructure for optimal performance.

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Enterprise cloud migration is a powerful strategy that empowers organizations with scalability, cost-efficiency, and enhanced security. By following best practices and aligning cloud strategy with business goals, enterprises can unlock the full potential of the cloud. However, enterprises need competent partners like Shopdev to navigate the complexities of this transformational journey successfully.

Artificial Intelligence
7 Mistakes to Avoid When Selecting an AI Development Company in 2024
5 min read

In the fast-evolving landscape of artificial intelligence, selecting the right AI development company is more crucial than ever. As AI technologies become integral to business operations across various industries, the stakes for choosing a competent and reliable AI partner have soared. The decision you make can either propel your business forward by leveraging cutting-edge AI solutions or set you back due to mismatches in expertise and expectations.

With AI advancements rapidly progressing each year, businesses must approach the selection process with a well-informed strategy. However, common pitfalls often trip up even the most astute business leaders. From failing to conduct adequate research to overlooking crucial aspects like scalability and data security, these mistakes can lead to disappointing outcomes. In this article, we will guide you through seven critical mistakes to avoid when selecting an AI development company in 2024, ensuring that your business makes a choice that aligns with its long-term technological and operational goals.

1. Insufficient Research

The foundation of a successful partnership with an AI development company is thorough research. Yet, many businesses rush into decisions, swayed by superficial evaluations or compelling sales pitches. This oversight can lead to a poor match between your project's requirements and the chosen company's capabilities, potentially resulting in project failure.

To avoid this, it's essential to dive deep into the backgrounds of potential AI partners. Start by examining their past projects, client testimonials, and independent reviews. Understanding their track record in detail will help you gauge their success rate and reliability. Websites like Clutch or Glassdoor provide comprehensive reviews that reflect real customer experiences and can be invaluable in this phase.

Also, consider reaching out to past clients directly. This can give you unfiltered insights into the AI company’s ability to deliver on promises and handle challenges. Learning from the experiences of others can steer you away from companies with a history of projects that do not meet client expectations.

Finally, do not overlook the importance of case studies. These often contain detailed descriptions of how the company approached different challenges and the outcomes of their projects. A company that can demonstrate a history of innovative solutions and successful implementations across various scenarios is more likely to adapt and thrive in your specific context.

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2. Unclear Objectives

A fundamental error many companies make when embarking on an AI project is not having clear, defined objectives. This lack of clarity can cause misalignment between your company's needs and the AI development approach, leading to outcomes that don't meet your expectations.

With 77% of companies either using or exploring AI, it's evident that businesses are increasingly dependent on AI to enhance various aspects of their operations, such as operational efficiency, customer experience, and product innovation​ (National University)​. This trend underscores the necessity of having well-defined objectives to effectively leverage AI for these purposes.

Before you start looking for an AI partner, it's crucial to outline what you want to achieve with AI. Are you looking to improve operational efficiency, enhance customer experience, or perhaps innovate a new product? Clearly articulating your goals will guide you in selecting an AI company that specializes in the specific areas you need.

For instance, if your goal is to integrate AI into customer service operations, you would look for a company with proven expertise in natural language processing and chatbot development. This specificity will narrow down your options and increase the chances of success.

It's also important to communicate these objectives clearly when engaging with potential partners. Clear communication ensures that the AI development fully understands your expectations and can provide a tailored solution that meets your specific requirements.

To facilitate this, consider creating a detailed project brief that includes not only your goals but also any constraints, such as budget and timeline. Share this document with potential AI companies during your initial discussions to ensure everyone is on the same page from the start.

Defining and communicating your objectives is not just about avoiding missteps; it's about paving a path to a successful AI implementation that is aligned with your strategic business goals.

3. Neglecting Industry Expertise

Choosing an AI development software solution company without considering its expertise in your specific industry is a critical oversight. Each industry presents unique challenges and regulatory requirements that require specialized knowledge. A company proficient in AI for finance may not be as adept in AI applications for healthcare, for example.

When selecting your AI development partner, it’s crucial to verify their experience and success in projects within your industry. This can involve reviewing case studies that highlight their work in similar sectors or requesting references from past clients in your industry.

Furthermore, consider the specific challenges your industry faces. For instance, if you are in healthcare, data privacy is a paramount concern, governed by strict regulations like HIPAA in the U.S. An AI company with a deep understanding of these regulations will be better equipped to ensure compliance and secure handling of sensitive data.

Inquiring about the technology stack the company uses is also essential, as some technologies might be more suited to particular industries than others. For example, a company using advanced neural networks would be preferable for image recognition tasks commonly required in the security or automotive industries.

To effectively assess a company’s industry expertise, prepare questions related to their past projects, the challenges they faced, and the solutions they implemented. This will help you gauge whether they have the practical experience and in-depth knowledge necessary to handle the specific needs of your industry.

4. Overlooking Data Security

Ignoring data security when selecting an AI development solution can expose your business to significant risks. Data breaches can lead to financial loss, legal repercussions, and severe damage to your company's reputation. It's crucial to ensure that the AI company you choose has robust security measures in place.

Start by inquiring about the data security practices the AI company follows. This includes understanding their data encryption methods, how they manage data access, and their compliance with relevant data protection regulations, such as GDPR or HIPAA. A reputable AI company should be transparent about their security protocols and willing to discuss how they will protect your data.

Additionally, consider the security of the technology stack they use. The programming languages, frameworks, and infrastructure should all meet the highest security standards to prevent vulnerabilities. For example, ask if they employ secure coding practices and regular security audits to detect and mitigate potential security issues.

It’s also wise to discuss how the AI company handles data breaches, should they occur. Understanding their incident response plan can give you confidence that they are prepared to act swiftly and effectively to minimize any damage.

To assess their commitment to data security, request to see certifications or attestations such as ISO 27001 or SOC 2. These certifications demonstrate a level of dedication to maintaining high-security standards.

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5. Ignoring Scalability

Scalability is often underestimated during the selection of an AI development solution. As your business grows, so will your demands for AI capabilities. Selecting a partner that can scale solutions to match your evolving needs is essential for long-term success.

To ensure scalability, assess the potential AI company’s infrastructure and technology stack. Ask about their experience handling increasing volumes of data and user engagement. A company that has successfully scaled projects similar to yours is likely to replicate that success with your project.

Moreover, discuss future growth prospects during initial meetings. Understand how the company plans to accommodate your growth, such as adding more resources or utilizing cloud technologies that offer flexibility. This discussion will give you insight into their capacity for adaptation and innovation.

It's also crucial to consider the scalability of the AI models themselves. Inquire about the AI company’s approach to updating and refining AI models as more data becomes available and as business needs change. The ability to continually improve and adapt AI models is key to maintaining the relevance and effectiveness of AI solutions.

Lastly, review case studies or testimonials that demonstrate the company’s track record with scalable AI projects. These will provide concrete examples of their ability to expand and adapt their services in response to client growth.

6. Inadequate Post-Development Support

Neglecting the importance of post-development support is a common oversight that can undermine the long-term success of AI projects. Once an AI solution is deployed, it requires continuous maintenance, updates, and support to ensure its effectiveness and relevance over time.

When selecting an AI development company, evaluate their commitment to post-launch support. This includes regular updates to the software to incorporate the latest AI advancements and patches for any security vulnerabilities that may arise. A company that offers comprehensive support will help ensure your AI system remains robust against evolving threats and technologies.

It's also important to discuss the specifics of the support agreement. Determine what types of support are available—such as 24/7 customer service, on-demand technical assistance, and routine check-ups. These services are crucial for quickly resolving any issues that might arise, thereby minimizing downtime and maintaining operational continuity.

Furthermore, ask about the company's procedures for handling feedback and implementing changes. An AI development that actively seeks and responds to client feedback demonstrates a dedication to service quality and continuous improvement.

Finally, consider the scalability of support services. As your business grows, your support needs may increase. The right AI development partner will offer flexible support options that can be scaled to match your evolving requirements.

7. Poor Communication and Collaboration

Effective communication and collaboration are pivotal for the success of any AI project, yet they are often overlooked when selecting an AI development. Poor communication can lead to misunderstandings, misaligned objectives, and project failures.

When evaluating potential AI partners, prioritize those who demonstrate strong communication skills right from the initial interactions. Assess their responsiveness to your inquiries and the clarity of their communication. This can be an indicator of how they will engage throughout the project lifecycle.

During your discussions, observe whether the AI company seeks to understand your business context and objectives thoroughly. A partner who asks insightful questions and proposes ideas that align with your vision is likely to be a collaborative and communicative team player.

It's also important to consider the company's tools and processes for project management and communication. Do they use established platforms like Slack, Microsoft Teams, or Asana? Are their processes structured to ensure regular updates and feedback loops? These tools and processes are vital for maintaining clear and continuous communication.

Moreover, inquire about the involvement of project managers or points of contact who will manage the relationship. Having a dedicated person or team to handle your project ensures that there is always someone available to address your concerns and coordinate between different teams.

Finally, discuss the company's approach to conflict resolution and problem-solving. Understanding how they handle challenges and setbacks can provide insight into their ability to maintain a productive working relationship even under pressure.

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Selecting the right AI development company is a critical decision that can significantly influence your project's success. Rushing the selection process without adequate research, or partnering with a firm that lacks the necessary industry expertise, can derail your goals. Clearly defining your objectives, prioritizing data security, and ensuring scalability are essential steps.

Equally important is securing comprehensive post-development support. Regular updates, technical assistance, and adapting to your growing business needs will ensure your AI solution remains relevant. Effective communication and collaboration, facilitated through strong project management and regular feedback loops, are vital for seamless project execution.

Avoiding these seven mistakes will not only help your business choose an AI development partner that aligns with your goals, but will also set the foundation for a long-term, successful partnership. By being diligent in your selection process, you can safeguard your investment and fully harness the transformative power of AI.

Artificial Intelligence
7 Steps to Build a Generative AI Solution from Scratch in 2024
5 min read

Generative AI is a revolutionary advancement in the tech world that has taken businesses and decision-makers by storm. With everyone using AI models like ChatGPT and Stable Diffusion to speed up development processes, it's safe to say that generative AI is here to stay. 

Jasper, one of the successful generative AI tools, has raised $125 million at a valuation of $1.5 billion by assisting businesses with copywriting. Similarly, Stability AI has made $101 million with a valuation of $1 billion. But the most significant achievement is that of OpenAI, which generated over $1 billion from Microsoft at a valuation of $25 billion in 2019. 

Clearly, the scope for generative AI solutions is broadening. Read on to learn everything you need to know about how to build a generative AI solution from scratch. 

What is Generative AI?

Back in the 1950s, researchers first explored the basic principles of artificial intelligence and generative AI. The initial focus was on simple neural networks and rule-based models to mimic human decision-making. 

On the surface, generative AI is an exciting new development where deep learning algorithms are used to create content, graphics, music, and more. Large datasets are used as input for deep learning algorithms that can generate unique content. What makes generative AI special is its ability to generate entirely new and unique outputs, unlike traditional AI which only delivers pre-programmed outputs.

When trained, the AI algorithms learn patterns from given inputs, store them, and utilize them to create unique content for similar inputs. Even though it looks easy, generative AI becomes quite complicated when you dig deeper. For instance, there are various components like transformers, generative adversarial networks (GANs), and variational auto-encoders behind the functionality of generative AI. 

Examples of transformers include ChatGPT, LaMDA, Wu-Dao, and GPT-3. Transformers are trained to process natural language and images, as well as learn classification tasks and generate texts. These techniques are designed to mimic cognitive attention and understand the difference between varying inputs.

What Can Generative AI Do?

Generative AI is helping businesses worldwide by generating content in the form of text, images, and videos. This means voice-over and background music in videos are not a problem anymore. Generative AI is also used in data augmentation to boost the accuracy of predictive machine-learning models. As a result, retailers can offer highly personalized experiences to customers. 

The following are some of the diverse application areas of generative AI across various industries:

Art and Creativity

The new, state-of-the-art algorithms of generative AI enable you to convert any ordinary picture into an art piece. You can guide the AI tool to add your unique style, features, and other favorite items. Even when you provide the generative AI tool with a rough sketch of the image you want to create, it generates photorealistic masterpieces that go beyond your expectations. Not only that, but the AI can also imitate specific styles of a given human artist, which allows you to create art like never before.

Content Generation

When it comes to automating content creation, you don’t need to look further than generative AI. It has provided marketers with the ideal solution to save time and resources. With automated content generation, you can achieve faster time-to-market and create a variety of campaigns. For instance, you can create social media posts and blog articles, and even design email campaigns. 

AI tools like ChatGPT, Jasper, Copy.ai, and Writesonic have accelerated the process of content generation. In some cases, marketers can personalize and refine drafts generated by AI models. Not only that but generative AI can also be used to update existing content with valuable inputs and improvements.

Natural Language Processing

Generative AI is commonly used in natural language processing to create social media posts and news articles. Various natural language processing techniques are used to generate the text for such posts. These techniques includes transforming raw characters like words, letters, and punctuation into sentences. The resulting entities and actions are represented in the form of vectors through multiple encoding mechanisms.

Image Synthesis

Today’s businesses want to represent their products and services with realistic graphics and lifelike images. Here, AI algorithms assist businesses with accurate image synthesis. You can eliminate misleading aspects from your images and fill in the missing elements to portray your products in the best way. 

Medical Imaging

That’s not all - AI tools can also convert low-resolution images into high-resolution images, making them look like they were captured by professional photographers. With generative AI by your side, your images are transformed into clear and detailed masterpieces. This is particularly useful in medical imaging analysis, where accuracy is of utmost importance.

Generative AI is used in medical imaging for specific tasks like image segmentation, anomaly detection, and prediction of patient outcomes. 

Anomaly Detection

Business owners need to go through large amounts of data to understand patterns in order to make informed decisions. In this case, generative AI helps detect anomalies in a series of data. Assuming a certain range of data based on historical trends, AI can point out values that fall outside that threshold and happen less frequently.

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How Generative AI is Changing Industries

The world knows generative AI as a buzzword because of its impact on different industries. It has quickly transformed business operations and is creating new opportunities across the board. Apart from creating novel, unique content and making way for innovations, generative AI is changing the way processes are carried out.

Here are some examples of how generative AI is transforming various sectors:

Entertainment and Media

Two of the many benefits of generative AI are democratized content creation and simplified management. In the entertainment industry, AI helps accelerate research tasks and allows filmmakers to improve the efficiency of their post-production operations. The data processing power of AI makes video libraries more searchable, making life easier for broadcasters. For this reason, the market size of generative AI is expected to grow to $12,077 Million in 2032 at a CAGR of 26.7%


Whether it is medical diagnosis, imaging, or analysis of a large number of patient records, AI is always good news for the healthcare industry. You can now leverage AI-based disease prediction and diagnosis to improve healthcare operations. Generative AI monitors various factors like lifestyle risk factors, medical imaging, patient health data, environmental factors, and genetic data to provide accurate results. Moreover, statistics show that 27.5 percent of health systems currently use conversational AI, with a further 72.5 percent considering it for future use.

Marketing and Advertising

Until a few years ago, marketing and advertising agencies required graphic designers and content creators to create their campaigns. But now, AI is being used by 53% of IT companies for marketing and advertising. With generative AI, this process is a matter of a few prompts. You can prompt AI tools to create unique and original graphics for blog posts, social media posts, and more. 


Game development is a complex and time-consuming process that involves various tasks. Like other industries, generative AI becomes useful in game development by automating complex tasks. These include creating assets, levels, and generating dialogues. By utilizing generative AI to handle these tasks, game developers are able to unlock creativity and use the extra time to work on the strategic aspects of their development cycle. Generative AI in the gaming sector is expected to achieve the highest market value with a valuation of $7,105 Million by 2032.

Manufacturing and Design

Now here’s an industry that focuses on optimized production and quality controls along with great customer service. By processing raw data and helping in the production of valuable products, generative AI significantly improves manufacture and design operations. According to Gartner, by 2025, generative AI will account for 10% of all data produced, up from less than 1% today. It helps enhance customer service, streamline production processes, and also improves quality control procedures. 

The Right Tech Stack to Build a Generative AI Solution

Before getting into the process of building your own generative AI solution, you need to know the right tools and technologies to use. The following is a comprehensive list of the tech stack you will need to use:

Deep learning framework TensorFlow, PyTorch
Generative Model Architectures GANs, VAEs
GPU Acceleration CUDA, cuDNN
Data Processing Pandas, NumPy
Image Processing OpenCV, PIL
Cloud Resources Amazon Web Services (AWS), Microsoft Azure, Google Cloud
Model Deployment Docker, Kubernetes, TensorFlow Serving, Flask, FastAPI
Version Control GitHub or GitLab
Experiment Tracking TensorBoard, MLflow

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7 Steps to Build a Generative AI Solution from Scratch

In order to build a generative AI solution, you need to develop a comprehensive understanding of its purpose and the problem it will solve. The process involves creating and training AI models to generate unique outputs depending on varying inputs. 

7 Steps to Build a Generative AI Solution from Scratch

As today’s generative AI solutions are capable of optimizing and improving operations, building a satisfactory solution requires you to follow a series of steps.

Problem Identification      

Since you are dealing with a lot of computing power in generative AI, you need to be sure that they are designed to solve the right problem. The first step in building your generative AI solution from scratch is to identify a set of problems to solve. For instance, marketing agencies need various types of content for their blog and social media platforms. In this case, your AI solution needs to be built around natural language processing, neural networks, and generative pre-trained transformers. In other cases such as video and audio generation, you will need to adopt a different approach.

Data Collection  

Data collection is one of the important steps in the prototyping phase of generative AI development.  Since the training of your generative AI solution depends on data, you need to consider certain technical details. These include:

  • Identifying the right data sources
  • Ensuring that high quality, relevant, and diverse data is collected
  • Labeling the data if needed using crowdsourcing, semi-supervised learning, or active learning
  • Preprocessing data through data augmentation, tokenization, or normalization before it is fed to the AI model
  • Splitting the data into separate categories for training, validation, and testing
  • Storing the data in data warehouses, distributed file systems, or cloud storage depending on the scenario.

Model Selection          

Based on the defined problem that the AI solution is intended to solve, you need to select the right model and tech stack to be used in the development. To be able to choose the right model, you need to categorize the problem and determine the right algorithms to solve it. 

For instance, if you’re working with input categorization, you need to select the supervised learning model. For imaging systems that can differentiate objects from images, you can work with OpenCV. On the other hand, you can also use Jupyter Notebook to allow seamless collaborations between machine learning engineers, developers, and data scientists.

Architecture Design       

In order to ensure that your generative AI solution can process large amounts of data and handle high traffic, you need to base it on a robust and scalable architecture. The common purpose of all generative AI solutions is to maintain high performance, reliability, and availability in all situations. The best practices to build the right architecture are as follows:

  • Break down the AI solution into small, manageable components through microservices architecture
  • Adopt a modular design where each module performs its specific function
  • Incorporate message queues to efficiently manage communication between different components of the AI solution
  • Implement load balancing to ensure equal traffic distribution across multiple servers and get peak performance
  • Implement caching to reduce backend requests by storing frequently accessed data
  • Configure hyperparameters of the generative AI model to set number of layers, learning rate, batch size, regularization techniques, dropout rate, etc.

Training and Validation        

In the training phase, you feed the AI model with datasets you previously collected and categorized. With these datasets, the generative AI model learns patterns that are later used to solve problems and create content. In order to train generative AI models, you need to use self-supervised learning and semi-supervised machine learning methods. Although the process is machine dominant, you need to add the human touch to fine-tune and set the AI model’s accuracy and align it with your business objectives.

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Deployment and Integration            

While deploying your AI solution, you need to be sure of its reliability, scalability, and maintainability. This is particularly important to eliminate errors and failures and ensure the smooth functioning of your generative AI solution. Here’s what you need to consider in the deployment and integration phase:

  • Choose the right deployment environment by setting up on-premise infrastructure, cloud computing systems, or hybrid solutions depending on the requirements.
  • Implement the right hardware and software for installing operating systems, setting up databases, and configuring servers.
  • Set up dependencies like libraries, frameworks, and packages required to run your AI solution.
  • Use tools like Jenkins, Travis CI, or GitLab to set up continuous integration and deployment (CI/CD) pipelines.

Monitoring and Maintenance

The last step of the process holds equal significance. Upon deployment, you need to constantly provide support and maintenance while evaluating how the AI solution performs in real-life use cases. The key is to identify bottlenecks and failures in order to fix and improve the solution. Upon further testing, you can refine your AI solution by studying user feedback and performance benchmarks.

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Pro Tips for Crafting Effective Generative AI Solutions

Even when you follow the best practices to build your AI solution, there are always going to be some unexpected issues and challenges along the way. Based on research in the field of generative AI, here are a few pro tips that can be helpful.

  • Apart from identifying a problem to solve, you should also identify specific business processes that can be enhanced by using generative AI. Focus on small changes with meaningful outcomes.
  • Ensure data protection to provide users the confidence to use your generative AI solution and stay compliant with international regulations. Use data encryption to reduce risks of data leaks.
  • Instead of spending big bucks on training AI software like ChatGPT, you should build smaller, more targeted AI models. These can be trained to solve business problems unlike ChatGPT.
  • Since the quality of data matters, your focus should be on preparing the data for training the AI model. Try to eliminate under-generalization and bias.
  • Regardless of how well you train generative AI, it can never be perfect. Therefore, you need to inform your users about the limitations of your AI solution.

Costs of Generative AI Solution Development

Depending on the type, complexity, and size of the application you are trying to build, generative AI development becomes quite a costly process. This means you can expect a bill between $10,000 and $500,000. The most notable costs include training the AI model and deploying it. The software cost itself is between $35,000 to $120,000, while maintenance costs can go beyond $320,000 per year. 

That’s not all, you also need to invest in specialized hardware and cloud storage to get the right computing power and storage capacity. Depending on the complexity of your solution, this can cost you between $100 and $15,000 a month.

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Can I make my own generative AI?

You can create your own generative AI by adopting, modifying, and building the models you require. However, the process is quite complex and you need an increasing number of resources as you continue building your generative AI. You also need to have technical expertise in the fields of natural language processing, machine learning, and computer vision.

How do I start generative AI?

First of all, you need to understand the different generative AI models available in the market. You should consider the unique features and applications of GANs, autoregressive models, and VAEs. You need to gather and prepare the data you want to work with and choose the type of AI architecture to use. Then you need to train your AI model and evaluate, optimize, and test it in a real-world environment.

Does generative AI use NLP?

Yes, generative AI leverages natural language processing to create new outputs or content for each input. The use of NLP means that you can also generate all types of content including text, images, audio, and video.

Is generative AI free to use?

There are free versions of generative AI tools like ChatGPT 3.0 and DALL-E 2. These are great for beginners who want to try generating content using AI. AI tools by OpenAI allow you to generate content by simply creating an account on their website and entering your prompts in the search bar.

Key Takeaways

In the era of artificial intelligence, businesses are being driven by generative tools that enhance operational efficiency and also help target the right customers. With businesses adopting generative AI solutions, there really is no limit to what can be achieved in the near future. We can expect unprecedented business growth with the help of efficient processes, ease of use, and the automation of complex operations. Based on the steps discussed in this blog, you can build your generative AI solution and embrace its transformative power in your business use cases.

7 Computer Vision Use Cases in E-Commerce for 2024
5 min read

Modern technologies are transforming the way people shop online by influencing their purchase decisions. With 88% of customers attributing product images to a great shopping experience, the use cases of computer vision in e-commerce continue to grow.

Computer vision allows retailers to provide buyers with new ways to search for products. Simultaneously, sellers can leverage automated inventory management, quality control, and much more. The technology is a form of artificial intelligence and works by deriving meaning from digital images just like humans - sometimes even better. It replicates the way we visualize products and performs image recognition, distinction, and classification using AI.

In this post, we discuss 7 computer vision use cases in e-commerce. Read on to find out how cutting-edge AI technology is reshaping the digital landscape.

The Role of Computer Vision in E-commerce

Customer expectations are changing, and so are retail businesses. The industry now recognizes the need for displaying interactive content in the form of product images and descriptions to improve the customer experience.

Computer vision is mostly dependent on high-quality images uploaded by sellers and customers on the internet. Digital marketplaces use it to match the items a customer searches for - and quickly provide them with the right results. Not only that, but computer vision also helps verify the uploaded images to determine the quality of the product in question. As a result, shoppers can feel secure knowing that the platform is offering the right products. 

The use of AI makes product search a breeze as you can display images dynamically to deliver an exceptional retail experience. You can automate repetitive tasks and save money. As AI enables computers to see and perceive images like humans, online shopping will become much more convenient in 2024. It improves engagement and you can have more loyal customers. 

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Use Cases of Computer Vision in E-Commerce

Visual Search Capabilities

Up until a few years ago, online shopping started with a text-based search query. Users had to go through a lot of products to find what they wanted. However, the introduction of AI technology has changed that. Visual search is one of the biggest leaps we have seen thanks to computer vision in e-commerce. It is an entirely new way of searching for products online. 

Visual Search Capabilities

Amazon’s Shop the Look interface

Users can simply upload a photo of the product they want to buy, and AI brings up all the relevant items in the store. A survey showed that 62% of Gen Z and Millenial customers in the UK and US want visual search capabilities in their online shopping journeys.

Even though it might not have made its way into all e-commerce stores yet, those who have experienced it do vouch for it. Amazon and eBay are two big names that leverage computer vision for visual search. Amazon’s StyleSnap provides the best-matched results for products when users upload a photo, and eBay customers can also search among 1.1 billion products using photos instead of text-based queries.

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Automated Inventory Management

Inventory management is something that every retailer wants to ace. However, the days of manual inventory counts and stock management are over. Since your customers expect swift delivery within a day, you need to be on top of your inventory game at all times. 

Here, computer vision helps you meet customer demands and maximize your sales. It allows you to analyze visual data from your inventory and order stock accordingly. The visual analysis helps you stay ahead of the curve by maintaining a steady stock level. Whenever a new order is placed, you don’t have to keep your customer waiting. You don’t have to worry about running out of stock or overstocking your warehouse. 

Automated inventory management with computer vision helps create a seamless shopping journey. Neither do you miss any sales opportunities, nor do your customers face any delays. Automating the inventory management process also streamlines replenishment operations to ensure that popular products are always in stock.

Augmented Reality Shopping

In retail, augmented reality shopping has taken the e-commerce experience to a whole new level. AR with computer vision goes beyond virtual try-on experiences to completely transform the way customers view their products. 

In augmented reality, computer vision recognizes objects in visual data through object detection. It uses object tracking to understand movements and count objects, and Simultaneous localization and mapping (SLAM) to localize an AI bot on a map. These complex processes collectively make up the immersive shopping experience we see on AR shopping apps.

Augmented Reality Shopping

Features like augmented product displays and virtual showrooms make it easier for customers to understand what they are paying for. They can explore the product in detail, compare the different options, and even take a virtual walk-through of the store.

Computer vision in AR also provides you with valuable insights into customers’ preferences. By incorporating AR into your e-commerce store, you can create a dynamic shopping environment and enhance the customer experience.  

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Quality Control and Inspection

Businesses in the retail industry compete on various fronts, but nothing beats quality products. When it comes to fashion, electronics, or other products that are assessed by visual characteristics, you need to ensure quality standards. 

Thankfully, quality control is another use case of computer vision in e-commerce. Here, computer vision is used for visual inspection of products at different stages of the supply chain. You can incorporate it at the manufacturing phase, the distribution phase, and when the product reaches your store shelves.

The quality control process involves computer vision algorithms that detect imperfections or defects in products. They perform visual inspection and compare products to a set standard to determine whether they meet the quality requirements. 

Incorporating computer vision for quality control allows you to improve customer satisfaction and lower your return rates. When customers get the same quality of products from your brand, they begin to trust it and become loyal customers.

Customer Behavior Analysis

Moving on, here is another important use case of computer vision in e-commerce. In retail, customer behavior analysis is the key to improving the customer experience. The more you know about your customer's preferences, the better you can assist them. 

In this use case, computer vision analyzes customer data to provide you with valuable insights to understand their shopping behavior. Integrating this technology into your e-commerce platform allows you to track customer activity across the store and understand how they interact with each aspect of your store. 

This includes product pages, the checkout page, the homepage, and more. However, computer vision is not limited to your online store only. In the physical store, it uses heat map technology to detect real-time movements and assign colors based on traffic volume. This helps retailers understand which sections of the store are more crowded and how they can optimize product placement, store layout, and more.

Shipment and Logistics Optimization

The last stages of the supply chain are the most crucial for retailers, as they can make or break customer trust. In shipment and logistics, computer vision is used to extract data from package labels and detect damages to parcels. It helps quickly identify and sort parcels depending on your business constraints. 

The AI system can sort by size, destination address, shape, or category of products. You can track packages in real-time within your store and while they are on their way to the customer. Real-time tracking with computer vision helps eliminate instances of misplaced items. 

Moreover, the technology helps you automate and simplify various operations in the dispatching and shipment of products. On a larger scale, computer vision can monitor activities on ports to detect suspicious activities that lead to misplaced parcels. 

Product Recommendations

Product recommendations are one of the best ways to engage customers online. Every time a shopper visits your e-commerce store, they see products on the homepage before they even begin their search. Computer vision makes this possible by analyzing the users’ search history, items they’ve previously purchased, and similar data from millions of other users.

According to a survey, 91% of customers are more likely to shop with brands that remember their shopping history and provide relevant suggestions.

In the e-commerce use case, computer vision algorithms process large volumes of user data to provide accurate personalized recommendations. As a result, your customers feel that your brand takes care of their preferences and displays highly relevant content. Finding the right products on the homepage of an online store also means that customers have to spend less time searching. 

With the help of pattern-matching algorithms, computer vision analyzes customers’ online journeys to determine the key areas where they might need helpful suggestions. Sometimes, users simply browse through your store without the intention of buying anything. Here, you can convert them into customers with the right product recommendations. Not only that, but these kinds of suggestions can also help upsell and cross-sell slow-moving items.

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Benefits of Computer Vision in E-Commerce

Beyond the common use cases, computer vision exceeds expectations to deliver an enhanced customer experience. Starting from product classification based on visual attributes, and going towards predictive analytics, the technology has transformed e-commerce for the better.

  • Brand detection

Computer vision utilizes advanced algorithms and machine-learning techniques to identify retail brands in images and videos. 

  • Effective market analysis

With the help of visual detection, brands can understand the popularity of a given product in the market and restrategize.

  • Predictive Analytics

The technology analyzes customer behaviors and preferences to create and display personalized product recommendations.

  • Enhanced customer experience

Your customers can find their products by uploading images and don’t have to go through a long list of products to find what they need.

  • Operational efficiency

Computer vision helps identify efficient routes for the supply chain and makes it easier for you to manage your e-commerce operations.

  • Security

With fraud detection capabilities, computer vision helps eliminate the risk of misplaced items, unscanned items, and employee theft.

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Embracing the Future of E-Commerce

Computer vision and the other subsets of AI have quickly become a necessity in the e-commerce landscape. With customers expecting a smooth omnichannel shopping experience, the applications of computer vision seem to be increasing quickly. In the retail sector, the technology has already automated checkouts, monitored queues, and analyzed foot traffic.

The buzzing technology has come out of the research phase to become a highly useful e-commerce tool. While there is no comparison between an AI-based system and a manual process, the benefits of computer vision in e-commerce retailers are too obvious to ignore.

Artificial Intelligence
The Ultimate Guide to AI App Development Using Generative AI
5 min read

App development has come a long way in recent years. As AI pushes the boundaries of innovation in software development, generative AI app development has become one of today's biggest buzzwords.  In fact, it has the potential to boost business profits by up to $4.4 trillion a year.

AI changes how we conceive and build our apps by unlocking unimaginable possibilities. Using large language models, developers can create intuitive in-app experiences. You also get to personalize user experiences with enhanced creativity.

Read on to find out how the ground-breaking AI technology enhances app development. 

How Generative AI differs from other AI technologies

AI has been here for quite some time in the form of machine learning and computer vision. However, these technologies are limited to analyzing and classifying datasets. Generative AI is capable of creating new content based on existing data. This can be in code, text, images, video, music, and more. 

Generative AI is currently being used for the development of innovative apps in different industries. 

Generative AI can be used to generate code for mobile apps, which can save developers a lot of time and effort. For example, DhiWise has introduced the new feature “WiseGPT” which analyzes the entire codebase to produce personalized production-ready code without writing prompts. The other is the “GitHub Copilot” tool that can generate code snippets and even entire functions based on natural language prompts.

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The Role of Generative AI in App Development

Generative AI offers an unparalleled ability to test apps for bugs and errors. AI-based systems not only identify security risks but they can also be used to write code for a wide range of applications.

Enhancing user experience with Generative AI

When it comes to online websites and applications, user experience is crucial. You need to have seamless transitions between different responsive, user-friendly pages. Here, generative AI can automate repetitive tasks in app development. 

As a result, it can solve problems without human input. You get an exceptional user experience with a bug-free application that doesn’t require any expert developer insights. That said, the effectiveness of generative AI apps depends on how well you train them.

Personalization and customization through AI

Personalized content is the way to go in the modern world. And nothing creates personalized and customized content like generative AI. By analyzing user history including search and preferences, AI creates personalized recommendations. This enhances the user experience by creating more opportunities for customer engagement. 

Generating content and creative elements automatically

When humans work in cross-functional teams, the output is great. Imagine AI doing the same, except with much higher efficiency. Generative AI raises the bar of creativity and innovation. It produces content that would take us too much time to create. Plus, we would need to use various methods and tools to come close.

Automated content generation takes app development to a whole new level. You can now uncover new concepts before even creating the application. Using novel patterns, styles, and combinations, AI allows you to create perfection. 

Improving app development with predictive models

Predictive analytics is a field of AI that allows developers to optimize app functionalities. By factoring in the time, effort, and cost of app development, it provides a clear pathway. You can easily identify potential risks and hurdles. It also allows you to eliminate bottlenecks to enhance the functionality of your application. These measures reduce the likelihood of issues arising during the development life cycle.

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Getting Started with Generative AI App Development

Before getting started with generative AI app development, you need to know its basics. The best practices allow you to be more productive in the process by making it smooth. Whether it's your first time or you’ve been working with AI, here are the steps for app development with generative AI:

Gather high-quality data

No matter how powerful an AI tool is, it still needs to be trained using existing data. The quality of this data determines how well generative AI will perform. Therefore, the first step is to gather error-free and well-structured data. Make sure it does not have any inconsistencies or biases. And of course, it should be relevant. 

Training your AI model using high-quality data yields accurate outputs. This makes app development with generative AI worth your while. And your app performs the intended functions with maximum effectiveness. 

Choose the right tools and frameworks

Choosing the right algorithms and AI models is among the critical aspects of using generative AI. You need to ensure that you choose the right one based on your specific use case. For example, if you want to generate natural language texts, you can work with algorithms like GPT-4. But if you want images as output, you need a deep learning model like GANs. If you make the right decision, your generative AI app will perform effectively and yield quality outputs.

Understand the data requirements for training AI models

Moving on, you need to ensure that your AI model operates at maximum efficiency. For this, technical know-how of data requirements is crucial. You can adjust the AI’s learning rate, batch size, and epochs, and even regularize it. Here, 

  • Batch size means how much data it can process at a time. 
  • Epochs mean the number of times it learns from the data. 
  • Regularizing means you can prevent it from overthinking or overprocessing data.

Fine-tuning your AI models results in high efficiency of the application.

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Building Your First Generative AI App

Building a generative AI app is a complex process that requires you to follow a unique set of steps. Here’s how you can get started with your first app:

Building Your First Generative AI App

Selecting and preparing your dataset

The process of developing an AI app starts with data. You need to identify the right sources to extract the datasets you’ll be using. This can be done using databases, web scraping, sensor outputs, and APIs depending on your requirements. This is a crucial step as the quality of the data directly affects the performance of your AI model.

Apart from data sourcing, you also need to pay attention to data diversity. The more diverse your data, the better the AI will perform. Its outputs will be more unique if you train it with different scenarios, modalities, and environments.

Training your Generative AI model

This is the step where your efforts shine through. Using the data you’ve gathered and cleaned, you need to train your generative AI model. You’ll be using deep learning and neural networks to feed the prepared data to the AI. It then learns to identify and emulate data patterns.

When the initial model is sufficiently trained, you can proceed to fine-tune the AI. Here, you can go deeper into specific tasks to generate the desired outputs. For instance, you can fine-tune your AI model to create poetry. You’ll have to train it on an extensive dataset of poetic content. Here, you can also deploy techniques like differential learning rates. This process takes different layers of the AI through different learning rates. 

Integrate the AI model into your app

Once your AI model is ready to go, the next step is to integrate it into your app. For this, you need to develop APIs or connectors. APIs act as a bridge that fills the gap between your app and the generative AI model. Then, you need to design workflows to ensure that the data generated by AI is integrated into your existing processes. Frameworks like FastAPI and Flask are commonly used to deploy APIs with AI models.

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Testing and optimizing app performance

Using real-world conditions and comprehensive testing techniques, you need to test the AI app performance. This step helps you determine whether your training in the AI model has been fruitful. 

Testing an AI model for performance requires validation and data metrics. With metrics like the ROC curve, AUC score, and error analysis, you need to assess the quality of the AI model's outputs. Moreover, you can compare the outputs with benchmark models to see where your AI model falls behind in terms of accuracy and performance.

Advanced Techniques in Generative AI App Development

Improving Model Accuracy and Efficiency

An AI model is like a work in progress. You can keep making changes and updates to tweak its performance. In the case of app development, you can improve your AI model with the following techniques:

  • Retrain the model on better data: You can use newer, more accurate data to retrain your AI. This helps improve performance as the outputs are based on the data being fed into the model.
  • Change the way you deploy the model: Sometimes an AI model can struggle to perform well if it isn’t hosted on the right platform or connected to the right hardware.
  • Source code enhancement: Some applications are just poorly designed and implemented right from the start. You need to review the source code to make the necessary improvements.

Scale your Generative AI app for a larger audience

Like any other tech solution, AI applications also need to be scalable for future use. Scalability challenges are common in AI app development. For instance, the size of datasets used to train the AI model, the sources used, and the deployment strategy need to be considered.

To make your generative AI app scalable, you need to:

  • Ensure that you standardize the development and deployment processes
  • Dedicate team efforts to tasks that they know best
  • Utilize tools that support creativity, efficiency, and security
  • Use development methods that allow updates for the future data

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Ethical Considerations and Best Practices

For quite some time now, ethical concerns have been raised against generative AI. The most notable concerns like bias and racism were seen when OpenAI’s ChatGPT was first released. For you generative AI app development, you need to consider the following best practices:

  • Bias check: Your AI application needs to be checked for unwanted biases that it may have picked up while training on different datasets.
  • Fairness: Your AI model must not discriminate or generate biased outputs that negatively impact different user groups
  • Anonymization: You need to take care of data privacy when user data is involved. So its important to anonymize your inputs and outputs at all times

Ensure privacy and security in AI-powered apps

Speaking of privacy, there are also reports of concerns related to data encryption. You need to monitor your app’s performance to identify vulnerabilities. Cyber attackers try to take advantage of the same AI capabilities in a negative way. For this reason, you must never include credentials and tokens in the code.

Apart from data encryption and authentication mechanisms, you must implement ongoing vigilance systems to avoid security breaches.

  1. Create ethical usage guidelines for your AI app.
  2. Use techniques like adversarial training to restrict bias
  3. Routinely monitor your AI app’s performance 
  4. Identify and remove fake content, news, and images.

Best practices for transparent and responsible AI usage

Once the ethical and privacy concerns are addressed, you also need to consider transparency in AI usage. For this purpose, you need to implement the following best practices:

  • Documentation: Create detailed descriptions of the AI models’s capabilities, its limitations, and how it is expected to perform.
  • Feedback loops: You need to have open channels to discuss and mitigate any concerns and questions arising from stakeholders.
  • Anomaly detection: Create systems to identify and take care of unintended behaviours or harmful outputs of the AI app. 
  • Guidelines and policies: The guidelines and policies for AI use must be updated each time a new feature is added or whenever the AI is retrained.

Navigate legal considerations in AI app development

In today’s day and age, the last thing you would want is a non-compliant AI solution. When it comes to legal considerations, you need to look at the following factors:

Copyrights: Businesses have started to use AI applications to create copyright content. As copyrights only protect content created by humans, this causes legal concerns. You need to ensure that your generative AI app cannot be used to create copyright content.

Confidentiality: Similarly, users and businesses are using AI tools for processes that involve personal information. To avoid legal consequences, you need to ensure that the personal data is either anonymized or used safely.

GDPR compliance: According to the GDPR, you need to define the data subjects and the purpose for gathering data. If you are unable to define the purpose, you cannot proceed with the training of your AI models.

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The Future of Generative AI in App Development

AI technologies are developing at an alarming rate. Here is what the future holds for AI-based app development:

Emerging trends and technologies in Generative AI

Based on OpenAI’s latest update called GPT-4 Turbo, the system has two core improvements. One is its ability to ‘pull from a newer database of information’. And the second is its promise ‘to follow instructions better’. This gives us an idea of how fast AI is growing and improving.

AI is already working with features we expected it to perform yesterday. So it’s safe to say that the future is already here. AI can now analyze extensive documents and provide you with a summary. This is because it can now support up to 128,000 tokens of context.

That’s not all, as GPT4 Turbo now also has new API assistants. These are aimed at simplifying the creation of AI-driven apps. You will get capabilities like a Code Interpreter, Retrieval, and improved function calling. For app developers, these features are absolute game-changers.

Predictions for the future impact of Generative AI on app development

According to Forrester, ‘generative AI is accelerating the pace of innovation’. If there is going to be a challenge, its going to be navigating the risks of AI. This is because of the exponential rate at which AI is learning. That said, there is good news too. AI will unlock higher levels of productivity in the years to come. 

  • By 2027, 15% of applications will be automatically generated by AI without human input.

The same research suggests that by next year, 

  • ‘40% of enterprise applications will have embedded conversational AI, up from less than 5% in 2020’.
  • By 2025 ‘30% of enterprises will have implemented an AI-augmented development and testing strategy, up from 5% in 2021.’

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Key Takeaways

In summary, generative AI has tremendous potential to accelerate application development. It suggests code, identifies bugs, and even refactors lines of code. You can command it to write simple applications to speed up app development. Not only that but you can also perform quality checks of your existing code.

Combined with the innovative ideas brought forth by generative AI, you can achieve higher customer satisfaction. The best part is that generative AI applications are built without ever gathering any requirements. 

5 Best Shopify ERP Integration Solutions for 2024
5 min read

As an e-commerce business, you understand the importance of operational efficiency. So you optimize your order fulfillment process by automating a labor-intensive workflow.

Thankfully, your Shopify e-commerce store integrates with ERP software to give fruitful results. This transformative move consolidates all your business processes in one place. You get better financial oversight, streamlined operations, and improved inventory management.

This article helps you choose the best Shopify ERP integration without any research. Our top 5 picks leverage advanced technologies to meet evolving e-commerce needs. Read on to find the answers you’ve been looking for.  

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Why You Need to Integrate Your Shopify Store with an ERP Solution?

Without a Shopify ERP integration, your Shopify store handles inventory, orders, and payments. In contrast, your ERP takes care of the supply chain, accounts, and HR.

Integrating your ERP solution with your Shopify store unlocks higher operational efficiency. The integration synchronizes data from your e-commerce store and all other business operations. You can:

  • Perform data entry across all channels 
  • Access all your data and information in one place
  • Manage supply chain and inventory
  • Handle accounts, HR, payable, and other operations
  • Process orders and payments

Challenges Faced without a Shopify ERP Integration

Let’s say you are running your ERP independent of your Shopify store. You have to manually manage your inventory counts and update them in the ERP system. Then, you have to insert the same information in your e-commerce store. 

When your business grows and sales go up, manual data entries will increase. With more items in your inventory, keeping a manual count becomes labor-intensive. This leads to inventory discrepancies and manual data entry errors. Moreover, your eCommerce operations become inefficient and difficult to scale.

How ERP Integration with Shopify Gives You a Strategic Advantage

An e-commerce business has numerous operations running simultaneously. Imagine having complete oversight and control over each of them. That’s the kind of power you get by integrating an ERP with your Shopify store.

Real-time Data Analysis

You can analyze sales trends and, expenditure patterns, and calculate profit margins using your ERP. It can generate financial reports automatically, saving you time and money. Modern ERP solutions offer advanced real-time data analysis features. This means you can take full control over your eCommerce finances. All you need to do is integrate the ERP with your Shopify store.


E-commerce businesses have to process and move large amounts of data each minute. According to 67% of organizations, ease of use is one of the most important features of an e-commerce platform. This is made easier with the use of an ERP system. Leveraging automation, you can streamline data exchange between different channels and touchpoints. This eliminates human errors in customer data, shipping details, inventory, and accounting. 

Centralized Control of Business Processes

What’s better than having all your business data and communications under one platform? A Shopify ERP integration centralizes your store data and ensures hassle-free communications. This means all departments and channels can use real-time data and enable flexible solutions. Without this integration, every department manages its data separately. 

As a result, when teams collaborate, there is the hassle of duplicate data and repetitive requests for information. With an ERP integration, you can create a secure, efficient, and error-free business workflow.

Choosing the Right ERP Integration for Your Shopify Store

Like any other business solution, ERP integrations are available in abundance. However, choosing the right one is the key to enhancing workflow efficiency. So, the following are the factors you need to consider for a successful integration. 


First, you need to know whether the ERP system is compatible with your ecosystem. It needs to align with your business processes and goals. For instance, if your business has a high order volume, you’ll need a more efficient system. 

It should be able to process large data volumes to fulfill orders quickly. You also need to determine whether the ERP system offers industry-specific features. The more the solution is tailored to your specific industry needs, the better.


You’re not running your e-commerce business to stay where you are. Tomorrow, the business will become an enterprise. So, scalability is one of your top priorities. The ERP integration you choose must be able to cope with the changing volumes of data. 

It should come with the necessary upgrades to adapt as your business grows. This means having technical support and continuous updates to the system. Not to mention, the process of updating the ERP should be easy for your teams.


Not all Shopify integration solutions support the customizations on your e-commerce store. If you’re the kind of brand that loves customization, you must consider the right solution. Some regular integration strategies won’t work for you. 

In that case, you need to consider tailored ERP solutions that allow a certain degree of customization. That way, you can get the most out of your Shopify ERP integration.


Like any other tech solution, ERP software is often too complex to understand. Not everyone operating the system will be an expert. So you need to consider a service provider that offers reliable support. This includes technical support, training, and customer support. 

You must be able to get assistance in setting up and using the integration effectively. But that’s not all. When it comes to streamlining your business processes, customer support plays a huge part. Consider this your top priority if you want to smoothen your integration process.


Initially, you might feel that investing in a new ERP integration solution is adding to the cost. However, in the long run, you will see that it's a fruitful investment. You get full control over your inventory, which allows you to order supplies efficiently. You only spend on what is needed. 
That being said, you still need to consider the best value for money. Choose an ERP solution that offers the best price for features that fulfill your requirements. At the end of the day, it’s your business that matters.


With artificial intelligence reshaping e-commerce integrations, adaptability is key. You need a solution that works - both now, and in the future. With AI and machine learning, customer engagement and data analytics will be better than ever. Invest in an ERP solution that is designed to cope with these future trends. 

5 Best Shopify ERP Integration Solutions for 2024

XStak E-commerce ERP 

XStak is an all-in-one, self-service ERP integration solution for e-commerce businesses. It transforms your business with omnichannel capabilities through multiple productivity modules. You get more than 50 integrations with the best ERP and e-commerce solutions. That means you can seamlessly integrate and customize your Shopify store. 

What’s more? XStak’s cloud-native and modular architecture makes it a scalable and hassle-free solution. A powerful cloud-based POS, coupled with an omnichannel order management system keeps you ahead of the competition. You also get marketing, payments, and business intelligence tools with a transaction-based pricing plan.

Inventory management is a breeze with real-time, multi-location synchronization of online and offline channels. The integrated warehouse and logistics management system automates order packing and delivery. Whether you consider scalability, customization, or support, XStak checks all the boxes. And it also prepares your business for the future with an AI-driven e-commerce assistant. 

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Dynamics 365 Business Central

Dynamics 365 is a cloud-based solution by Microsoft known for its CRM capabilities. It offers various tools for marketing, sales, project management, and marketing. With financial tracking and project service automation, you can streamline your business processes. 

The ERP integration solution goes beyond basic CRM capabilities to offer data-driven insights. As a result, you can improve efficiency with the help of predictive analytics. If service automation isn’t enough to convince you, there’s more. Microsoft Dynamics 365 is a future-proof solution that comes with AI, machine learning, and mixed reality tools. 


Brightpearl is another cloud-based ERP solution that comes with a comprehensive set of tools. Designed to serve small and medium e-commerce businesses, the solution handles multiple operations. These include inventory management, sales, purchasing, customer relationship management (CRM), and accounting. 

What makes Brightpearl stand out from the competition is its real-time inventory management system. The functionality updates your inventory counts across all channels. This means whenever a purchase is made, the inventory is updated in your e-commerce store, physical store, and marketplaces. 

Acumatica ERP

When it comes to managing your e-commerce operations efficiently, Acumatica ERP is a great choice. It is an ERP software that provides various business management applications. The cloud-based solution offers financial management with project accounting and distribution management. 

Acumatica ERP also offers customer data management as well as manufacturing management. All the data is recorded in real-time. This provides you with real-time visibility into your business processes, which in turn helps make data-driven decisions.

NetSuite ERP 

NetSuite ERP is another popular choice among e-commerce businesses. Most commonly integrated with Shopify, the ERP solution offers all the great business management functionalities. On top of the CRM, HRM, and inventory management features, you get high customizability. 

You can even download data from a given period of dates. The user-friendly dashboard makes business management a breeze. Whether it is accounting or order management, all your data is accessible anywhere with cloud storage. However, the license cost of this solution increases with an increase in users. 

How to Implement an ERP Integration with Shopify

  1. Identify your business requirements: Regardless of the ERP integration you choose, you need to lay out your specific business requirements first. This is done by analyzing your business data and the complexity of your operations. You also need to look at pain points and set a goal to be achieved.
  2. Clean your business data: Before implementing a new ERP integration, it is important to clean, normalize, and validate your data. You also need to eliminate duplicate entries and review who can access each data set.
  3. Select middleware: Next, you need to select a middleware solution to integrate your Shopify store with the ERP system. Middleware solutions automate the integration process. These include Zapier, Jitterbit, and Dell Boomi.
  4. Configure integration: The ERP configuration process requires you to map data fields between the two systems. In this step, you need to define workflows and synchronize your business data.
  5. Testing and deployment: Upon testing the integration within your e-commerce system, you can proceed with deployment. For this, you need to perform data synchronization, test workflows, and train your teams.

Future Trends in ERP and E-commerce Integration

Increasing Automation with AI

As AI and machine learning techniques become more advanced, they will continue to enhance e-commerce processes. We can expect higher levels of automation when it comes to ERP integrations. This will be seen particularly in the areas of automated data entry, order management, and inventory management. 

Increased adoption of Cloud-based ERP Solutions

Here’s another future trend that we can already see coming true. E-commerce businesses love cloud-based ERP solutions. Thanks to their flexibility and cost-effectiveness, these solutions will see even higher adoption in the coming years. We see numerous businesses transforming their operations with cloud-based solutions. Not only do these systems offer efficiency but they are also easier to integrate into your Shopify store.  

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Key Takeaways

In a world of automation and centralized business processes, Shopify ERP integration is a necessity. It lays the foundation for a successful e-commerce enterprise. By bringing together data from different channels, it improves efficiency and team collaboration. Not only that, but you can also add more value to customer journeys.

To avoid the complexity of integrating an ERP solution, it is important to choose the right tech partner. Shopdev provides unmatched technical expertise to help e-commerce businesses reach new heights.

Omnichannel Excellence: How an OMS Transforms Retail with Cegid Integration
5 min read

Running a retail business in a world of cross-functional teams and agile organizations is tricky. Your customers expect a convenient shopping experience regardless of the platform or medium they use. And all this can only mean one thing - a heated competition for omnichannel OMS excellence in retail. 

The key to winning this competition is omnichannel retail.

It's a strategic approach that enables retailers to provide a seamless customer experience across all channels.

Naturally, for retailers, cross-channel consistency is critical. Selling your products through online channels alone is not enough. 

Read on to find out how you can take your retail business further with a leading OMS and Cegid integration. 

Omnichannel Excellence in Retail Commerce

According to research, 50% of customers search for products through online marketplaces. This means brands that run only brick-and-mortar stores have already lost a lot of leads. 

Moreover, 59% of customers like shopping through social channels. And a further 71% like to complete the checkout from within the social channel. 

You need to be present everywhere and provide consistent services across multiple channels. This is where omnichannel retail comes into play. Incorporating an omnichannel OMS like XStak order management system allows you to create a seamless customer experience across all devices and touchpoints. 

The power of XStak OMS shines through when integrated with Cegid’s cloud-native commerce platform. While XStak OMS synchronizes inventory across all channels, Cegid Retail improves customer engagement. You can offer a personalized, seamless omnichannel experience to customers anywhere in the world.

Omnichannel Excellence by the Numbers

Consistency Across Channels

No customer enjoys a shopping journey where the brand experience varies from one channel to another. Moving from a social marketplace to a retail website must be a seamless and consistent experience. Otherwise, today’s customers will waste no time before deflecting to another brand. 

Incorporating XStak OMS with Cegid Retail integration helps you achieve omnichannel OMS excellence. Your customers get a cohesive brand experience regardless of the devices, channels, or touchpoints they use. They are also able to view consistent product catalogs and availability statuses throughout their journey. 

Integration of Services

Keeping up with the fast-paced retail environment requires a high level of automation and accuracy. Your eCommerce business cannot rely on separate third-party applications to run its operations. 

Fortunately, omnichannel order management systems have service integrations that help streamline and automate operations. Integration of services within an OMS allows retailers to improve performance and reduce operational costs.

For instance, XStak’s omnichannel OMS comes with integrations for payments, logistics, ERP, marketplaces, and more. This means you can provide seamless services across multiple sales channels. Using a single OMS, they can perform all tasks from product search to payment processing, checkout, and delivery. 

Similarly, Cegid Retail integrates with top-tier tools and solutions to enhance the retail experience. Examples include Expensya integration for expense management and Booxi for appointment scheduling. 


Every time we hear about a successful retail brand, we see a common offering - tailored experiences. With AI-based data analysis, retailers can utilize customer data like never before. The better the data analysis, the better the personalization.

Personalization helps retailers achieve omnichannel excellence by building trust and loyalty. 

And what’s better than having an OMS that personalized customers’ experiences? The answer is an OMS with Cegid Retail integration. 

XStak’s OMS analyzes customer data to create personalized shopping experiences. Customers can view real-time inventory and track their orders from any location. 

Simultaneously, Cegid Retail improves engagement with a consistent experience across various channels. The integration helps improve your team’s efficiency so customers can enjoy a smooth experience. 

Real-Time Accessibility

Whether you consider order creation, inventory, or order fulfillment, you need real-time updates. Moreover, this real-time data should remain consistent across all your channels. Real-time inventory visibility helps avoid inconveniences related to product availability in different locations. 

Similarly, real-time synchronization of customer data helps achieve omnichannel excellence. Your retail brand can deliver the same experience even when the customer chooses to shop using another channel. 

Here, the integration of Cegid Retail with XStak OMS is particularly beneficial. It allows Cegid Retail to access real-time information. As a result, order processing and fulfillment are made as efficient as possible. With accurate metrics to work with, you can make well-informed business decisions.

Cegid Retail is a stable, secure, and accessible unified commerce platform that enables you to run your business operations worldwide. Whether it is day or night, your customers get the same brand experience.

Customer-Centric Approach

To make it big in retail, your operations must begin and end with the customer. The journey must be optimized starting from the time the customer engages with your store for the first time. 

An OMS helps achieve this by consolidating data from various touchpoints and providing a centralized view within one dashboard. By enabling customers to browse products online and complete their purchases in-store, you can improve engagement tenfold. 

Order management systems like XStak centralize customer data for all of your online and offline stores. Based on their shopping behavior and preferences, you can run targeted promotions and loyalty programs. Moreover, a centralized inventory allows you to provide realistic fulfillment times to customers.

These features of the OMS are further improved with Cegid Retail integration. It provides you a single view of inventory for all your store locations and enables your brand to achieve omnichannel OMS excellence. 

The Power of Integration: Cegid and Omnichannel OMS

The story of omnichannel excellence doesn’t end by just incorporating the right OMS into your system. You also need to know how to run your business based on the omnichannel model. This is where Cegid comes in. Cegid provides cloud-based management solutions to implement your retail projects. 

In simple terms, 

  • XStak OMS provides you with the tools to enable omnichannel retail.  
  • Cegid provides you with the expertise to manage your omnichannel model. 

This integration allows efficient order management, fulfillment, customer data management, personalization, and much more.

Cegid’s cloud-native POS

The primary aim of Cegid’s cloud-native POS is to provide a unified commerce platform to retailers. Bringing together and centralizing the data from all sales channels, Cegid helps boost profitability, increase operational efficiency, and improve customer engagement. 

With Cegid’s integration, you can accelerate your omnichannel transformation and optimize your supply chain. It’s even better if you have physical stores. Cegid helps improve the in-store customer experience with the help of employee training for better sales.

Omnichannel OMS

XStak’s distributed order and inventory management system is your key to synchronizing your offline and online channels. The omnichannel solution ticks all the right boxes by providing real-time insights. 

Automating inventory tracking across multiple stores is just the beginning. You get end-to-end order management, meaning you have clear visibility of the whole process right from the time an order is placed until it is delivered. 

When it comes to omnichannel excellence, XStak’s OMS streamlines and automates your operations to save time and reduce errors. 

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Enhancing Customer Experience

Today’s customers look for more than just their desired products when they visit your brand. They expect tailored experiences and minimum effort to process their order. A successful retail brand is one that understands these changing customer needs and delivers accordingly. 

Streamline Customer Journeys with Cegid and Omnichannel OMS

Here is how an omnichannel OMS with Cegid integration improves customer experience:

  • Analyze customer preferences and behavior using data from all channels using the OMS
  • Personalize the shopping journey by tailoring the interactions and recommendations at every step
  • Run targeted marketing campaigns with personalized emails and advertisements
  • Leverage Cegid to extend personalization beyond online channels
  • Bridge the gap between online and offline retail outlets by training staff through Cegid Retail
  • Integrate customer services to ensure that support agents have access to real-time customer data
  • Enable order customization and personalized fulfillment as per customers’ demands

Retail Challenges with Omnichannel Solutions

  1. DSW, Inc. (Designer Shoe Warehouse) is an American footwear retailer that implements the omnichannel model. It is considered one of the best omnichannel retailers, as it provides highly relevant search results. Customers can access tailored information as well as availing order-online-and-pick-up-in-store facility.
  1. Similarly, Abercrombie & Fitch has implemented an omnichannel model to overcome purchase barriers. Apart from online and in-store pickup, they keep cart contents consistent across all customer devices. This makes it easy for customers to browse on one device and check out from another later on. 
  1. The renowned fashion brand Zara has an omnichannel where customers can shop online, via mobile app, and in-store. But the shopping experience remains consistent. The brand has also incorporated RFID tagging to improve supply chain efficiency and improve customer experience. 

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Operational Excellence and Efficiency

As a retail business you are used to selling through different channels. So the key to achieving operational excellence is having one platform to manage all sales channels. Adopting an omnichannel OMS allows you to consolidate orders from multiple channels. As a result, you can manage and fulfill orders more efficiently. 

Similarly, adopting an omnichannel communication system allows you to seamlessly integrate emails, social media apps, and SMS communications. You get a unified system to handle all your channels, regardless of their type or location. 

Optimizing Operations with Cegid and Omnichannel OMS

L’azurde, a prominent jewelry brand in the Middle East, has achieved omnichannel excellence with the perfect solutions. The brand’s decision to incorporate XStak OMS with Cegid integration has significantly improved customer journeys. Integrating Cegid’s POS system with the OMS allowed L’azurde’s in-store staff to access online orders and process payments for efficient fulfillment.

This perfect marriage of XStak OMS and Cegid POS has also provided convenience to L’azrude’s customers in the form of:

  • Multi-site fulfillment
  • Buy Online, Pick up In-Store (BOPIS)
  • Reserve and Collect
  • In-store Sales via tablets

The jewelry store’s operations now face no hindrances or siloes. They create memorable customer experiences through consistent journeys across all channels.

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Empowering Teams for Better Performance

Achieving omnichannel OMS excellence is impossible without a well-trained team. Therefore, you need to align your team with your omnichannel strategy. Ensure that they understand the intricacies involved in delivering a cohesive customer experience. 

Cegid provides the right tools and training to your employees for this purpose. With team collaboration and training tools, they help coordinate in-store operations. Your staff gets a sense of shared responsibility to deliver an omnichannel customer experience. 

Competitive Edge in Retail with Omnichannel Strategies

Overall, we can see the retail industry adopting the omnichannel strategy to stay ahead of the competition. Based on customer expectations and market trends, personalized shopping experiences are the key to success. They help build brand loyalty and improve customer lifetime value.

Moreover, consolidated data from omnichannel platforms provides valuable insights to help retailers make informed decisions. The key to winning this competition is to maximize the physical presence by synchronizing the online channels using an OMS.

Meeting Consumer Expectations

In the highly competitive retail industry, it's the survival of the fittest. You must be ready to strategically handle customer demands while providing a seamless experience. Modern retail supply chains depend on automated inventory management systems for improved visibility and tracking. 

Omnichannel customers spend 15 to 30% more than single or multi-channel customers. 

When you have an omnichannel retail platform, you can run as many online and offline stores as you want. The more the stores, the better the customer experience. Customers will have more touchpoints to interact with and will enjoy the convenience. Meanwhile, your brand can benefit from higher traffic and sales.

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Key Takeaways

Omnichannel excellence is prevailing as a transformative approach that can help retail businesses stand out. It gives a whole new dimension to seamless customer experiences by providing new ways for interaction. 

Not only that, but omnichannel management systems seamlessly integrate various channels and touchpoints to ensure maximum customer engagement. 

Undoubtedly, this creates delightful shopping experiences that result in lasting customer loyalty. 

At shopdev, we help retail brands overcome the challenges associated with customer experiences and provide effective omnichannel strategies to overcome them. Check out this case study to learn more about our expertise in revolutionizing retail operations and elevating customer experiences.

Software Development
Custom Software Development Lifecycle: From Idea to Implementation
5 min read

Every so often, business leaders find themselves at a crossroads, adapt, or get left behind. One critical adaptation is investing in custom software, tailored to unique business needs. But diving into the world of software development can be daunting, especially when it feels like uncharted waters. So, how does one transition from a brilliant idea to a functional software solution?

The answer is simple, familiarize yourself with the Custom Software Development Life Cycle (SDLC). Today, having a digital edge isn't just an advantage — it's a necessity. 

This article discusses the 7 stages of the custom software development lifecycle in detail. You’ll get to learn the methodology, significance, and importance of each phase, as well as standard SDLC models. Our guide is designed for both seasoned business magnates and start-up trailblazers to ensure you make informed decisions. Now, let’s start our journey with the basics!

What is SDLC?

Software development life cycle (SDLC) in the true sense is a systematic process for developing highly efficient software. The process guides the development team to design and build software that meets and fulfills end-user requirements. The objective here is to minimize the risks and margin of error by dividing the procedure into different phases. Subsequently, each phase has its objectives and deliverables that feed into the subsequent phase.

What are the phases of SDLC?

The phase of the Software Development Life Cycle (SDLC) keeps changing over time as the industry is involved. The purpose of SDLC phases is to provide a consistent and systematic approach to software development. As a result, they ensure that all functional and user requirements are met as per set standards. 

When it comes to custom development, 7 stages of the software development lifecycle include:

  • Idea generation and conceptualization
  • Requirement analysis
  • System design
  • Implementation or coding
  • Testing and quality assurance
  • Deployment and release
  • Maintenance and continuous improvement

The 7 Stages of Custom Software Development Lifecycle

Embarking on a custom software development journey is akin to constructing a building. The process requires careful planning, execution, and maintenance on your part as well as your custom development partner. Below is a roadmap containing 7 stages of the software development lifecycle that you must pay attention to.

Infographic content: 

7 Stages of Custom Software Development Lifecycle

  1. Idea Generation and Conceptualization
  2. Requirement Analysis
  3. System Design 
  4. Implementation or Coding 
  5. Testing and Quality Assurance
  6. Deployment and Release 
  7. Maintenance and Continuous Improvement
7 Stages of Custom Software Development Lifecycle

Idea Generation and Conceptualization

At the heart of every innovative software lies a seed: an idea, a solution to a problem. This is where the journey begins. So, ask yourself what problem your software is solving or what value it's adding. Is it a novel tool for users or an automation of a manual process in the retail industry?

Evaluating the feasibility of your idea in the market is pivotal so pay attention to detail.

The initial phase of the software development lifecycle is all about brainstorming and gathering insights. So, define the broad objectives of your software project for better conceptualization. It’s wise to gather two cents from your stakeholders, business analysts, and potential users to shape the vision.

Discuss and talk about questions like: 

What problem are we addressing?

What's the potential market size?

What are the initial features?

If possible, conduct feasibility studies to assess the technical, economic, and operational viability of your software idea. By the end of this phase, you’ll be able to sketch out a preliminary concept of the software. This initial SDLC phase will help you dive deeper into specific requirements of software development.

Pro Tips

  • Market Research: Understand the needs of your target audience and the current solutions available.
  • SWOT Analysis: Analyze the strengths, weaknesses, opportunities, and threats of your software idea.

Requirement Analysis

Once your software idea is crystal clear, it's time to delve into the nitty-gritty. The second phase of SDLC involves gathering detailed requirements from stakeholders and understanding user needs. The requirement collection process will help you outline every function, feature, and constraint the software must possess. These could be:

Functional (what the software should do) 

Non-functional (performance, security, or user experience)

Requirement Analysis

For a better requirement analysis, you can conduct surveys, workshops, and interviews. And don’t forget to create a Requirement Specification Document as it’ll become the reference for subsequent phases. In essence, this stage lays down a clear roadmap of "what" the software should achieve.

Pro Tips

  • Engage Stakeholders: Regular discussions with business teams, potential users, and tech teams.
  • Document Everything: Use tools like Confluence or Jira to maintain a detailed record.

System Design

Transforming requirements into an actionable design is the heart of the System Design phase. In this stage of the software development lifecycle, architects and designers draft detailed blueprints for the software. These blueprints, often graphical, indicate how different software components will interact, data flow diagrams, database design, and more.

Two major activities involved are:

  • High-Level Design (HLD)
  • Low-Level Design (LLD)

High-level design involves outlining the main modules including their structure, components, and relationships. Low-level design delves deeper into each module, describing its functions and procedures in detail. Such a comprehensive system design ensures developers have a clear path to follow in the next phase.

System Design

Pro Tips

  • Prototyping: Create mock-ups or wireframes to visualize the software.
  • Feedback Loop: Regularly share designs with stakeholders to ensure alignment.

Implementation or Coding

The Implementation phase of SDLC, commonly known as coding, is where the rubber meets the road. Using the design documents as a guide, developers begin writing code in the chosen programming language. This is one of the most crucial phases in the 7 stages of the software development lifecycle.

Single or more than one team of developers develop each module of your custom software. The major part of the design phase is coding, but developers may also perform initial unit testing. The testing helps them ensure that individual components of the software work as intended. In this stage, the main focus remains on coding conventions, clarity, and comprehensiveness to ensure the software is both functional and maintainable.

Pro Tips

  • Version Control: Use tools like GIT to manage code versions.
  • Code Reviews: Regular reviews to maintain code quality and catch errors early.

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Testing and Quality Assurance

Before deploying, it's vital to ensure the software is bug-free and performs as expected. That’s why QA and testing is considered the heart and soul of custom software development. The quality assurance teams often use manual and automated testing tools during the QA process. They do rigorous testing against the requirements defined from functional and performance testing to security audits.

Testing and Quality Assurance

The whole testing phase is all about ironing out the kinks to ensure the software works smoothly. The most crucial testing methods are:

  • Unit testing (individual components)
  • Integration testing (interconnected components)
  • System testing (the software as a whole)

Pro Tips

  • Automated Testing: Utilize tools like Selenium for repetitive and large-scale tests.
  • Bug Tracking: Tools like Bugzilla can help in reporting and managing software defects.

Deployment and Release

Once tested rigorously and deemed ready for users, the software is deployed to a production environment. In this stage, the software is deployed on the intended platform and becomes accessible to the end-users. Depending on your custom software, this might be a full release or a phased one. 

Deployment and Release

Deployment in phases is a good choice if there's a large user base or if risks are perceived. However, software release or deployment is not just about making the software available. Preparing user documentation, and training materials, and sometimes conducting training sessions are also part of this phase of SDLC.

Pro Tips

  • Backup: Always keep backups before deployment to prevent any data loss.
  • Monitoring Tools: Use them to observe software performance in real time.

Maintenance and Continuous Improvement

Software, however meticulously designed and developed, will inevitably face issues in the real world. That’s why maintenance is the most crucial phase in the 7 stages of the software development lifecycle. So, if you are of the opinion that post-deployment is the End of SDLC, the journey isn't over.

Feedback from users, changing business environments, or technological advancements may necessitate software tweaks. It's about addressing post-deployment bugs, updating the software to accommodate changing requirements or improving performance. Apart from reactive measures, this phase also focuses on proactive enhancements. This phase ensures that the software remains relevant and functional over time.

Continuous improvement might involve adding new features, refining user interfaces, or optimizing backend performance. Periodic updates and patches ensure the software remains relevant, efficient, and secure.

Pro Tips

  • Feedback Channels: Open channels for users to report issues or request features.
  • Regular Updates: Schedule them to enhance features, fix bugs, or improve security.

What is SDLC methodology?

SDLC methodology refers to the framework that organizations use to structure different phases of software development. In essence, an SDLC methodology is a set of practices, techniques, procedures, and rules used by those who work in the field. Over the years, several SDLC methodologies have been developed, with each offering a unique approach to software development.

Common SDLC Models

There are several custom software development lifecycle methodologies and each is a different process in itself. Below we are discussing the common SDLC models used in the industry for a custom software development lifecycle.

Agile Model

Agile Model

The agile model follows an iterative approach as it divides the software development process into smaller increments or iterations. In this SLC model, attention is paid to customer feedback throughout the custom software development lifecycle. There’s a regular collaboration between the developing team and the end user. Since the model follows adaptive planning, the approach allows for changes and adaptations throughout the project.

Advantages of the Agile Model

  • Easily accommodates changes even late in the development phase.
  • Continuous involvement ensures the product meets the customer's needs.
  • Regular iterations allow for early detection and rectification of errors or changes.

Drawbacks of the Agile Model

  • Requires active user involvement
  • Can be costly

Waterfall Model

Waterfall Model

Contrary to the Agile model, this is a linear and sequential approach. In the Waterfall model of the custom development lifecycle, each phase must be completed before the next phase can begin. There's no overlapping or iterating of phases and the model emphasizes thorough documentation at each stage. All in all, it's a straightforward yet rigid process as changes are hard to implement once a phase is complete.

Advantages of the Waterfall Model

  • Easy to understand and use, especially for smaller projects.
  • Each phase completion is a distinct milestone.
  • Ensures clarity and can be beneficial for future reference or projects.

Drawbacks of the Waterfall Model

  • Inflexibility
  • Late Detection of Issues

Iterative Model

Iterative Model

The iterative model is considered ideal for those who are constantly updating features and functions. In this custom software development lifecycle, you start with a few basic features and keep improving through repeated cycles. Since each version is an improvement upon the last, it allows you to add features and fix issues.

Advantages of the Iterative Model

  • Initial versions can be released to gather feedback for improvements.
  • Even basic versions are often functional
  • Allows users to engage with the software sooner.
  • Allows you to test functions and features on the go.

Drawbacks of the Iterative Model

  • Requires careful planning
  • Can be time-consuming

Spiral Model

Spiral Model

It’s the custom software development lifecycle approach that combines both iterative and waterfall models. The spiral model emphasizes risk assessment at each cycle and is ideal for complex software that requires regular improvement.

Advantages of Spiral Model

  • Can be tailored to specific project requirements.
  • Continuous risk assessments ensure potential pitfalls are identified early.

Drawbacks of the Spiral Model

  • Complexity
  • Can Be Expensive

Big Bang Model

Big Bang Model

Among all common SDLC models, the Big Bang model follows the most unorthodox approach. It requires minimum planning and the model involves following a vague idea while evolving as development progresses. In this model, the developer team starts coding with an explorative approach, allowing the software to take shape as they go.

Advantages of the Big Bang Model

  • Offers developers a lot of freedom to innovate and try different approaches.
  • Especially when the end goal isn't strictly defined.

Drawbacks of the Big Bang Model

  • Unpredictability
  • Potential for High Risks

Challenges and Best Practices for SDLC

The SDLC provides a structured framework for software creation, but navigating through its stages is not without hurdles. The custom software development lifecycle is a complex process during which multiple challenges can arise. However, recognizing these challenges and adopting best practices can significantly smooth the path.

Challenges in the Custom Software Development Lifecycle

The most common challenges that one might have to face are as follows.

Requirement Ambiguities

 The primary challenge in the software development lifecycle is the clear and accurate gathering of requirements. Misunderstandings or vague requirements can lead to a product that doesn't align with the stakeholders' vision.

Scope Creep

As the development progresses, additional features or changes might be introduced, leading to a constantly expanding project scope. This can delay delivery times and inflate budgets.

Technical Debt

 Sacrificing quality for speed in the early stages can result in a pile-up of "quick fixes" or inefficient solutions. This technical debt can become a significant issue in later development stages.

Integration Hiccups & Testing Complexities

Integrating different software components, especially when developed simultaneously or by different teams, can bring forth unforeseen compatibility issues. Additionally, ensuring comprehensive testing that covers all possible use cases can be a daunting task.

Best Practices for a Smooth SDLC

The best way forward is to prioritize in-depth sessions with all stakeholders at the beginning of the project. It’s better to utilize questionnaires, interviews, and workshops to extract as much detail as possible. Furthermore, you should try to:

Document Everything

From the initial concept to the final system design, ensure that every decision, change, and functionality is well-documented. This will provide a clear roadmap for developers and future maintenance or iterations of the software.

Incorporate Iterative Feedback

Regardless of the SDLC model adopted, build in regular feedback loops with stakeholders and potential end-users. The approach will help in the early detection of misalignments, ensuring the final product resonates with user expectations.

Invest in Code Reviews

During the custom software development lifecycle, regular code reviews can drastically improve code quality. They help in identifying inefficiencies, potential bugs, or deviations from best coding practices. You should adopt a multi-faceted testing approach including unit testing, integration testing, performance testing, and user acceptance testing. 

Plan for Post-Deployment

Planning for post-deployment includes monitoring the software in its real-world environment. This can help you address any emerging issues and you can gather user feedback for further refinement.

Stay Updated with Technology Trends

The tech landscape evolves rapidly in the digital world, especially when it comes to the SaaS industry. So, stay informed about the latest technologies, tools, and best practices to leverage cutting-edge solutions. This way you can enhance software quality and get an edge over your competitors. The key to success is choosing a highly skilled development partner for custom software building.

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Custom software development lifecycle is a structured journey through various stages. By understanding and meticulously following all 7 stages of the software development lifecycle, entrepreneurs can make informed decisions. You need to consider the key factors to ensure that your software not only meets its intended objectives but also stands the test of time.

Artificial Intelligence
AI in Digital Marketing: Effective Use Cases
5 min read

Marketing is becoming a vital aspect of any company's business domain. It allows the company to thrive and expand. Without the right marketing strategy and roadmap, it would be almost impossible to attract and engage customers, which can cause a notable impact on the growth and even decline the revenue streams. Therefore, it is crucial to stick with the contemporary marketing trends to stay ahead of the rivals and perform better.

Modern times have brought incredible evolution to the marketing industry. The Internet has opened up many new opportunity corridors, and people are now focusing more on digital marketing than conventional marketing practices. In fact, digital marketing occupies almost half of the total marketing shares. One of the most notable advancements in digital marketing is integrating artificial intelligence and machine learning in marketing practices to ensure rational decision-making and smart effort in the right direction.

Use Cases of AI in Digital Marketing

AI for Automated Processes

Human insight is still crucial despite the skyrocketing trend of adopting AI technology. AI is becoming a primary aspect of a modern paid media strategy. By automating the iterative tasks, you can spare a lot of time and money that can be utilized elsewhere in the core business operations. Investing less time on iterative tasks can also boost productivity and performance.

AI for Content Creation and Curation

Producing quality content is a primary aspect of establishing authority within your industry or niche. Ensuring authority improves organic rankings and transparency in the Search Engine Results Pages - SERPs. Conventional Marketers may be reluctant to let AI take the controls and generate content autonomously, but this technology is closer than one may think. The New York Times, Reuters, and Washington Post already use AI to generate content. The Press Association, for instance, can now produce 30,000 local news articles a month using AI. You might think that these are formulaic like who, what, where and when stories, and you are right; some of them certainly are. But now, AI-produced content has expanded beyond formulaic production to more creative writing domains such as poetry and novels.

The methodology that automatically produces a written narrative from data is called natural language generation (NLG), which is already used for different content generation needs from business intelligence dashboards, business data reports, personalized email, and in-app messaging communication to client financial portfolio updates, etc. Now digital marketers will have more time to work on strategic growth plans, face-to-face meetings, and other core business areas where human assistance is more valuable than AI.

AI to Predict Customer Behavior

Predictive analytics is another of the most exciting use cases of AI for digital marketers. Many predictive analytics companies encourage users to visualize and analyze data in a secure environment, such as Microsoft Power BI, a self-service platform introduced for non-technical business users. Now you don't need to be an expert in big data to adapt to the new technology.

Microsoft Power BI

The accuracy promised by the AI tool is so overwhelming that you can predict customers' future behaviour based on past behaviour, data, and statistical models. In this aspect, AI is not only saving time but ensuring precision. When customers' actions can be predicted accurately, you can showcase them with highly personalized ads and notifications that will lead them through a buying funnel that improves engagement and boosts sales. In the same way, AI-based digital marketing can let you acknowledge your target market segment. Outlining your Ideal Customer Profile - ICP is crucial to extract the most out of your marketing budget because spending time on leads that aren't going to buy is a waste of time and money. As far as self-evaluation is concerned, Modern AI tools can analyze your sales history and quickly determine the firmographics and demographics to let you know where you can focus your efforts.

AI for Better Personalization

Personalization is no longer just a trendy buzzword as personalized messaging, as it is quickly becoming a norm. Most online consumers expect brands to personalize their messages, whether personalized based on matching their location, demographics, or certain interest. Many consumers won't even give attention to messages that are not personalized. By analyzing consumer data and behaviour, AI algorithms can actively personalize CX's customer experience to ensure consistent engagement and interest. Recommendation engines are one of the most prominent examples of AI technology by virtue of which you can easily adapt a more fruitful marketing strategy by knowing your customers' buying habits and interests. You can most commonly witness this technology used on eCommerce platforms and OTPs. Consider an example of Netflix. Their goal is to keep you interested in their subscription service. Before and after you finish your favourite season or movie, Netflix pushes recommendations and Top Picks for you to keep you engaged and refrain from cancelling.

The average user doesn't know about Netflix using AI to learn their watch history – they'll just be excited to start watching Dark as soon as they finish Stranger Things.

Amazon uses AI greatly by providing personalized recommendations based on buying history, items you have viewed or liked and items in your cart, etc. These recommendations are more useful once a user is already in the final stage of the purchasing funnel.

AI chatbots and Digital Marketing

Different AI methodologies such as semantic recognition and natural language processing are intended to improve customer experience and deliver a better response. Compared with manual customer service, AI chatbots have many perks. First of all, AI chatbots allow businesses to go one step further than the conventional "one-to-one" form of customer service. Instead, chatbots can promise "one-to-many" support by interacting with several customers simultaneously and in different time zones. Secondly, AI chatbots don't need to rest as humans do to answer customer queries 24/7 and in real-time with the same accuracy and speed. Not only does this mean they are highly efficient, but they can also deliver services outside of office hours. Last but not the least, customers can opt for the language used by AI customer service as per their needs and feasibility, allowing a brand to deliver a personalized customer experience. Many brands have begun to interact with their customers using messenger applications like WhatsApp, Facebook Messenger, and Slack, all of which can benefit from AI chatbots in order to automate the process and improve customer experience without involving human effort.

Chatbots for Customer Service

Chatbots, powered by AI, can communicate with customers in real-time, providing instant responses to inquiries and support requests. These virtual assistants are capable of handling a wide range of tasks, from answering FAQs to guiding users through a purchase process, enhancing the customer experience while reducing the workload on human customer service representatives.

Predictive Analytics

Predictive analytics leverages AI to forecast future customer behaviors based on their past actions. By analyzing data patterns, AI can identify potential future purchases, allowing marketers to tailor their strategies to nudge the customer towards a sale. This proactive approach in marketing ensures that customers are presented with products or services they are likely to need, improving conversion rates.

Content Creation

AI is now able to generate basic content, such as news articles, social media posts, and even some types of image content. While it may not replace human creativity, AI can significantly speed up content production, providing marketers with a tool to create more content in less time. This is particularly useful for maintaining a consistent online presence and engaging with audiences regularly.

SEO and Content Strategy

AI tools can analyze search trends and user queries to inform SEO and content strategies. By understanding what users are searching for, marketers can tailor their content to meet these needs, improving their visibility in search engine results. AI can also suggest topics that are likely to attract attention, helping content creators to stay ahead of trends.

Social Media Insights

AI's ability to process and analyze large datasets extends to social media platforms, where it can monitor mentions, sentiments, and trends related to a brand. This provides marketers with valuable insights into public perception, allowing them to adjust their strategies in real-time to capitalize on positive trends or mitigate negative feedback.

Email Marketing

In email marketing, AI is used to personalize email content for individual recipients, test different subject lines, and determine the optimal send times to increase open and click-through rates. By continuously learning from user interactions, AI can enhance the effectiveness of email campaigns, ensuring that messages are more relevant and engaging to each recipient.

Voice Search Optimization

As voice search becomes more popular, AI is helping marketers optimize their content for voice queries. This involves understanding the natural language patterns used in voice searches and ensuring that content provides clear, concise answers to these queries. Optimizing for voice search can improve a brand's visibility in voice search results, a growing segment of online searches.

FAQs on AI in Digital Marketing

Q1: What is AI in digital marketing?

AI in digital marketing refers to the use of artificial intelligence technologies to improve marketing strategies and efforts. This includes personalizing content, optimizing ad campaigns, automating customer service with chatbots, and much more, all aimed at enhancing efficiency and engagement.

Q2: How does AI personalize marketing content?

AI personalizes marketing content by analyzing user data like browsing history, purchase behavior, and preferences. It uses this information to predict what content or products a user might find interesting, creating personalized experiences for each individual.

Q3: Can AI write blog posts or create content on its own?

Yes, AI can generate basic content like blog posts, social media updates, and simple images. However, it's best used as a tool to assist content creators by providing suggestions or drafting content, rather than replacing human creativity entirely.

Q4: How do chatbots improve customer service?

Chatbots handle inquiries and support tasks 24/7 without human intervention, providing instant responses to customers. They can answer FAQs, guide users through processes, and even handle basic transactions, improving the overall customer experience.

Q5: Is AI in digital marketing expensive to implement?

The cost varies depending on the scale and tools used. Some AI tools and platforms offer cost-effective solutions for small to medium-sized businesses, while more advanced implementations might require a significant investment. However, the return on investment can be substantial due to increased efficiency and engagement.

Q6: How does AI help with SEO and content strategy?

AI tools analyze search trends and user behavior to suggest content topics and optimize for search engines. They can identify what users are searching for and help create content that matches these queries, improving visibility and engagement.

Q7: What is predictive analytics in marketing?

Predictive analytics uses AI to analyze data and predict future customer behaviors, such as potential purchases. Marketers use these insights to tailor their strategies, targeting users with content and offers that match their predicted needs.

Q8: Can AI manage social media accounts?

While AI can't fully manage social media accounts, it can help by scheduling posts, analyzing engagement data, and providing insights into trends and sentiment. This aids in creating more effective social media strategies.

Q9: How does AI optimize email marketing campaigns?

AI optimizes email marketing by personalizing emails for each recipient, testing different subject lines, and determining the best times to send emails. This increases the chances of emails being opened and acted upon.


AI is revolutionizing digital marketing by providing tools and technologies that enhance the efficiency and effectiveness of marketing strategies. From personalization to predictive analytics, AI is enabling marketers to engage with their audiences in more meaningful ways. As AI technology continues to evolve, its impact on digital marketing is expected to grow, offering even more opportunities for innovation and engagement. Marketers should stay informed about AI developments to leverage these technologies fully, ensuring they remain competitive in the ever-changing digital landscape.

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