The Platform
A GenAI platform for simplifying software maintenance and modernization meeting industry quality standards.
Adapting AI to Meet Enterprise Needs
Use AI with confidence and full control over your code
Your code is never shared or used for training
AdaptsAI ensures that there is no compromise on enterprise fundamentals including privacy and security of your codebase. Your organization’s code is neither shared, nor stored and most importantly never used to train our model.
You control how AdaptsAI is deployed
AdaptsAI can be deployed in a completely private and isolated environment deployed on your . Deploy AdaptsAI in the way that best suits your organization needs and policies: as secure SaaS, on VPC, or in your private cloud.
Secure by default, industry standard coding practices
The software artifacts generated by AdaptsAI engine are created keeping industry standard coding practices and are configured to be secure by design and secure by default. You can customize AdaptsAI engine to with your team specific best practices, policies, and engineering standards to modify the output.
Encryption, Security, Data Protection and more!
We encrypt all access / transactions between your systems, data and AdaptsAI infrastructure including data at rest and in transit. In addition, you have the option to encrypt using your own keys. All communications follow the latest TLS protocol and AdaptsAI follows industry standards for privacy and data protection.
Enterprise Grade Support
AdaptsAI offers 24/7 priority support, ensuring that businesses receive timely responses and expert guidance whenever needed powered by our dedicated customer success team.
Fast-track Your
Modernization Plans
Reverse engineer existing code to wiki, leading to high-confidence modernization at blazing fast turnaround times.
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.