TyKolt's profile on Product Hunt

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6d ago

How do you handle LLM hallucinations in your pipeline?

RAG retrieves the right documents, but LLMs still make up details not present in the source data.

I kept hitting this wall, so I built Kremis. It shifts the approach from probabilistic guessing to deterministic verification.

Here is what it actually does:

1. Ingests your data to build a strict knowledge graph.

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2d ago

kremis - Every LLM claim traced to real data — or rejected

LLM claims validated against real data — never invented. Kremis ingests EAV signals, builds a deterministic graph, classifies every response: FACT (direct edge), INFERENCE (derived path), or UNKNOWN (not in data). No confidence scores. No probabilistic gaps. Built in pure Rust (no async, no floats in core), with ACID persistence via redb, HTTP API, CLI, and MCP bridge for Claude/Cursor. Apache 2.0. v0.17.8 — alpha, functional, tested. 354 tests passing.