A robust LLM Governance & ROI Evaluation platform designed to benchmark Frontier models against local open-source models. Built with an enterprise microservices architecture and cloud-ready for Kubernetes, this tool helps organizations optimize AI spend by calculating the accuracy-vs-cost tradeoff of local vs. cloud inference - sochaty/llm-governance-engine
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Maker
📌
Hey PH 👋 Maker here.
This started from a real incident — I accidentally sent a customer's credit card number to GPT-4o during a benchmark test. Saw it in the logs the next morning. Already gone.
Spent the weekend building a firewall so it couldn't happen again. Every prompt gets scanned by Microsoft Presidio before it reaches any model. If PII is detected above a confidence threshold, the request is blocked — HTTP 403 — before it touches the wire.
The part I'm most proud of: the policy engine is a YAML file. No restarts, no code changes — edit the file, hit one endpoint, rules are live. Ship a policies/hipaa.yaml or policies/gdpr.yaml to your team, and your compliance posture is a git commit.
Happy to answer anything about the Presidio integration, the FastAPI dependency injection pattern, or the multi-provider routing. Ask away.