Agentic AI & MCP Platform
Built an internal AI platform that gives engineers and applications governed, auditable access to enterprise data through MCP, plus the retrieval and agentic workflows that run on top of it.
The Problem
Engineers and internal applications at a multi-plant manufacturer needed real answers from operational and ERP data, but getting them meant one of two bad options: write one-off SQL against a sprawling production schema every time, or paste data into public AI tools with no governance at all.
The AI experiments that did happen were ungoverned. No access control, no audit trail, no shared memory between sessions, and no reliable way to tell whether an AI answer was actually correct. Every new workflow re-solved data access from scratch, and nothing built on what came before. For a business where the data drives production decisions, that combination of no governance and no way to measure quality was a wall.
What I Built
An internal AI platform on Python and FastMCP that gives engineers and internal applications governed, auditable access to enterprise data and AI tooling through the Model Context Protocol (MCP). I built it with Claude Code as my primary development environment throughout. It is in daily use by the engineering team, with production deployment underway.
Governed MCP Tool Surface
The platform exposes SQL Server and operational data as governed MCP tools, so an engineer or an agent gets exactly the data it needs through a controlled, audited interface instead of raw database credentials or copy-paste. Every call is scoped and logged. A persistent context and memory store is scoped across personal, project, team, business-unit, and organizational levels, so knowledge accrues over time instead of evaporating at the end of each session.
Retrieval, Search & Evaluation
Designed the search stack over a Postgres and pgvector knowledge base: hybrid lexical, semantic, and contextual retrieval, with reranking, query rewriting, and metadata filtering. Added image and vision search over SOPs and technical diagrams. Built LLM-as-a-judge evaluation pipelines so retrieval and answer quality are measured rather than assumed, and regressions surface before users hit them.
Agentic Order Entry (Human-in-the-Loop)
Developing an agentic workflow that ingests customer purchase orders spanning hundreds of products and millions of configurations and produces production-ready manufacturing orders for human review before anything commits. It combines the platform's MCP tooling, Azure AI Foundry, model evaluation, and structured extraction. A large part of the work is upstream: driving standardization of engineering data so the AI has reliable inputs, because an agentic workflow is only as trustworthy as the data underneath it.
Governed AI Assistant
Designing a page- and role-aware AI assistant for the company's internal platform, powered by the platform's tool surfaces through Azure AI Foundry, with defense-in-depth access gating and per-user usage limits. The assistant can read and cite the right records for the person asking, and nothing else, by construction.