Our AI assists in updating legacy codebases by identifying outdated code patterns and suggesting modernization strategies, including migration to newer languages or frameworks, ensuring the codebase is efficient, maintainable, and scalable.

By understanding the intent behind feature requests, our AI automatically suggests boilerplate code, design patterns, and even implements significant portions of the new features, with automated build and test as part of the AI development lifecycle. This enables rapid prototyping and faster feature delivery with reduced manual coding efforts.

Our AI agents automatically scan codebases for vulnerabilities using an up-to-date database of known security issues, identifying potential flaws and suggesting targeted remediation strategies to address vulnerabilities before they pose a threat.

Our AI agents can be trained on any documented standard or regulation. By analyzing code(or any other digital artifact) for compliance issues, automatically generating reports on compliance status, and recommending modifications to meet a variety of requirements, the toil of compliance can be greatly reduced.

When integrated within the development ecosystem, our AI can identify bugs during build, test and deploy, drastically reducing debugging time. It highlights errors and suggests or automatically applies fixes for common problems based on context and developer preferences, accelerating the development cycle.