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Faster Modernization with GenAI

Legacy code to requirements to modern stack, saving 90% effort!

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Business Requirements

Features, Business Flow

Domain Specific Logic

Calculations, Protocols

Technical Needs

Cloud, Language, etc.

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Architecture Design

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Infrastructure as Code

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Implementation

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Test Suite

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Generate high-fidelity requirements from legacy code

Connect your legacy code repository and let AdaptsAI engine extract detailed set of business requirements as well as domain specific logic. Our engine doesn’t require additional documentation but can comprehensively reverse engineer any and all technical and domain specific requirements from existing code. The detailed requirements are documented and can also be visualized in terms of current architecture of your application - showcasing dependencies between different code components and entities.

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Review and Approve the PRD and Technical needs

The business requirements and domain specific logic/calculations are editable for you to review, modify and approve. You can refine the feature descriptions, any technical or domain related details to align with your project requirements - including output cloud infrastructure preference and code language for frontend and backend.

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Time for some AI magic to modernize your application

Once you approve and submit, unleash the power of AdaptsAI’s patented GenAI engine, to generate modern enterprise-grade architecture tech stack, infrastructure as code as reusable components, detailed implementation and automated tests along with full code documentation into your preferred code repository system like GitHub, Bitbucket, GitLab etc. AdaptsAI incorporates best practices for security and quality, ensuring generated code meets high standards, including engineering fundamentals integrity.

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Fast-track Your
Modernization Plans

Reverse engineer existing code to wiki, leading to high-confidence modernization at blazing fast turnaround times.

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Frequently Asked Questions

AdaptsAI uses its patented engine to parse your code into modules and leverages fine-tuned language models to generate detailed functional and technical specifications. Think of it as building a comprehensive knowledge graph of your codebase. We then use generative AI to produce artifacts—such as high-level requirements, architecture diagrams, sequence diagrams, and data models—that provide a complete picture of your system.
While AI chat assistants like ChatGPT work well for small sets of files, they often struggle with larger repositories. They typically cannot maintain complete context across an entire codebase, which limits their precision and coverage. In contrast, AdaptsAI’s patented engine is specifically designed to parse and understand your entire codebase, ensuring high quality and accurate documentation even at scale.
We understand that safeguarding your code is critical. That’s why we implement robust security protocols to protect your work. Your code is used solely to create detailed functional and technical specifications (along with other related documents) and is processed only temporarily. Once the results are generated, your files are automatically deleted, ensuring they are neither stored nor reused. Additionally, your code is never used to train or improve our AI models.
Our Code to Wiki solution produces a comprehensive guide to your codebase, complete with intuitive navigation and search capabilities. Moreover, we provide an AI chat assistant that has full context of the generated wiki. This assistant not only serves as an onboarding guide but also allows you to interact in natural language—making it easier to find the information you need. We believe that as AI assistants become more prevalent, traditional methods of consuming documentation will evolve toward more conversational, natural language interactions, whether through text or audio.
Our system continuously updates the AI assistant to reflect the latest changes in your codebase, ensuring you always have access to current information. Additionally, the overall wiki is refreshed on a periodic basis—frequency determined by your pricing plan—to capture all modifications accurately.
Yes. Our Code to Wiki solution is designed for scale—it can handle large codebases, including repositories with over 250MB of code spanning more than 3,000 files. However, for optimal clarity, we recommend generating documentation on a per-service or per-microservice basis rather than processing an entire monolithic repository at once. This approach ensures that the resulting artifacts remain clear and concise for each component.