As the volume of AI-generated code grows, developers lose deep, line-by-line understanding.
Developers regain ownership and expertise in large-scale, sprawling codebases with Kodou.
Kodou uses the build process to uncover structure: component execution boundaries, modules, and dependencies. It safely imposes order on sprawling repositories and strengthens documentation and tests.
Products with less robust engineering, built solely on ML or LLMs, often fail.
Scale Performance and Testing with our multi-language compiler and distributed runtime. The compiler controls the context of its integrated LLM, limiting it to the code of interest.
For Kodou, compilers are the backbone and leverage AI to safely guide development.
For example, Kodou generates a repository copy containing only the files relevant to the logic or functions of interest.
The AI context is in the pull request.
Software Decoupling: Scaling & AI Context
Decoupling is simplified with the Async Button concept. Kodou’s platform processes repositories to decouple both code and dependencies -- automatically enabling asynchronous behavior.
Performance scaling comes from service deployment.
Software testing is an ever-increasing burden.
Decoupling enables parallel execution and shortens test time by leveraging provisioned infrastructure.
Focused and curated selections of code create a bounded and semantically relevant context for LLM inferencing.
Decoupling enables this selection and isolation, ensuring the code can run standalone.
Decoupled Code: async code is packaged for deployment. The API mimics the function signature.
“solution to the growing software dependency conflict problem…”
“...allows for the recommended secure code to appear above other selections"
"Kodou solves the issue of enterprise technical debt with an innovative solution based on Akka"
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with Kodou
Solving your growing AI-generated code problems.