Enterprise AI Context Optimization & MCP Engine
Model Context Protocol (MCP) Architecture & Spec-Driven Development Framework
Architectural Overview
Pioneering enterprise AI-assisted engineering workflows by building context injection pipelines with Model Context Protocol (MCP) servers and vector embeddings to eliminate LLM hallucinations.
The Context Limitation Problem
Generic AI coding assistants frequently hallucinate when dealing with enterprise-grade microservice codebases due to missing domain context, outdated repository rules, and lack of awareness of Architecture Decision Records (ADRs). Raw prompting leads to architectural drift, brittle code, and developer frustration.
The MCP & Spec-Driven Solution
Architected an internal context optimization engine utilizing custom Model Context Protocol (MCP) servers integrated with vector databases. Dynamically parses AST trees, retrieves relevant domain invariants, and injects validated OpenSpec contracts into coding agents—reducing hallucinations by 60% and lifting engineering velocity by 35%.
AI Architecture & Engineering Toolkit
Measurable Engineering Impact
-60% Hallucinations
Strict domain schema injection and AST-grounded retrieval virtually eliminate invalid API calls and phantom libraries.
+35% Team Velocity
Engineers transition from manual boilerplate authoring to review-driven, contract-verified pull requests.
30+ Engineers Upskilled
Founded and led the Renault Digital "AI Dojo", championing prompt engineering, MCP servers, and Spec-Driven workflows.
Context Injection Pipeline Architecture
Architecture flow showcasing MCP context retrieval, vector embeddings, and agent prompt synthesis
