Kaizen
Autonomous Software Modernization and Quality Engineering for Legacy Systems.
The Context-Aware AI Engine for Massive Codebase Navigation and Governance.
CodeSavvy is a next-generation AI-powered development intelligence platform designed for the 2026 engineering landscape, where codebase complexity often outpaces human cognitive limits. Built on a proprietary Retrieval-Augmented Generation (RAG) architecture, CodeSavvy indexes local and cloud-based repositories to provide deep architectural insights rather than just simple code completions. It specializes in 'Contextual Intelligence,' allowing developers to query multi-million line mono-repos as if they were speaking to the original architect. By 2026, its market position has shifted from a simple 'copilot' to a 'codebase oracle,' integrating directly into CI/CD pipelines to enforce architectural standards and prevent technical debt. The platform utilizes a hybrid compute model, processing sensitive logic locally via WebAssembly while leveraging high-performance clusters for complex cross-repository dependency mapping. It bridges the gap between legacy documentation and real-time code evolution, ensuring that institutional knowledge is extracted directly from the source of truth.
Uses hierarchical vector embeddings to map code relationships across multiple repositories.
Autonomous Software Modernization and Quality Engineering for Legacy Systems.
Bridge the gap between natural language and complex database architecture with AI-driven query synthesis.
Add AI-powered chat and semantic search to your documentation in minutes.
Automated Technical Documentation and AI-Powered SDK Generation from Source Code
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Generates high-level summaries and impact assessments for every pull request using diff-stream analysis.
Natural language query engine that finds code based on intent rather than keyword matches.
Optional local-only vector database to ensure IP never leaves the developer's machine.
Visualizes the blast radius of potential changes across microservices.
Dynamically switches between LLMs (e.g., Llama 3, Claude, GPT-4) based on task complexity and cost.
Analyzes commit frequency vs. code complexity to identify high-risk areas.
Undocumented COBOL or old Java systems that no one currently employed understands.
Registry Updated:2/7/2026
New hires taking 4-6 weeks to become productive in complex mono-repos.
Production outages where the source of failure is buried in service interactions.