Hi HN — I built this and I’m explicitly asking skeptics to tear it apart.”

Interlock is a safety and certification layer for AI infrastructure, not an optimizer or a vector database.

The problem I am solving for is that AI systems (vector search, RAG pipelines, agent frameworks) don’t usually fail cleanly — they degrade silently, oscillate under load, or keep returning corrupted results until something crashes. Monitoring tells you after the fact; circuit breakers tend to be static and blind to context.

Interlock tries to address that by:

forecasting time-to-failure under stress

intervening before hard limits are reached

refusing to serve results when confidence collapses

producing cryptographically signed evidence of what happened (control vs protected runs)

It includes:

integrations with FAISS, Pinecone, Weaviate, Milvus, LangChain, LlamaIndex (Elasticsearch experimental)

TypeScript + Python support

automated stress tests (control vs protected)

long-run stability tests

certification classes (I–V) derived from actual configuration + behavior, not labels

Importantly: Interlock does not guarantee correctness or uptime. It certifies that a given configuration survived a defined stress test without crashing, oscillating, or serving degraded results — similar to a structural load rating rather than a promise.

The repo is fully open source, and all claims link to test artifacts and CI runs. I’m especially interested in feedback on:

failure modes this wouldn’t catch

where the certification model is too strict or too weak

whether this is actually useful in real production AI systems

Repo: https://github.com/CULPRITCHAOS/Interlock

Happy to answer questions or be told why this is a bad idea lol