Even when retrieval, guardrails, and safety tooling are in place, organizations still lack an independent way to verify whether an AI output itself is reliable enough to act on.
We built TruCite as a model-agnostic “verification layer” that sits after an AI generates output. It analyzes properties of the output (structure, internal consistency, uncertainty signals, citation patterns, and drift risk) and produces a numeric reliability score plus a human-readable verdict and audit trail.
This is not another RAG tool or fact checker. It’s meant to answer:
“Given this AI output, should a human or organization trust it enough to proceed?”
We currently have a live MVP that anyone can test with arbitrary AI-generated text. I’m sharing it here to get candid feedback from people working in AI safety, governance, legal tech, or regulated AI deployments.
Key questions I’m hoping HN can help with:
Where do you see the biggest near-term failure points for AI in regulated decision-making?
Would an independent scoring layer like this actually reduce risk in practice?
What would make this useful enough for enterprises to adopt?
Happy to answer questions and explain the design choices.