Every team has the same code review problem: PRs sit for days, reviewers miss subtle logic bugs, and security issues slip through because nobody carefully checked the authentication layer. Linters catch syntax and style issues, but they don't reason about intent. A language model can — and you can run it entirely on your own infrastructure without sending a single line of your source code to a third party.
This guide walks you through building a self-hosted AI code review tool in Python. It reads a git diff, sends it to a locally hosted language model, and returns structured review comments you can pipe directly into your CI workflow.
Why Self-Hosted Matters
Sending your source code to an external API is a significant trust decision. For proprietary code, regulated industries, or anything security-sensitive, you want model inference happening inside your own perimeter. Ollama handles this cleanly: it runs any GGUF-quantized model locally and exposes an HTTP endpoint that's fully compatible with the OpenAI Python SDK. You get the same API surface, zero data egress.
The architecture is intentionally simple:
