QueryStory Raises $6M to Make AI Answers Trustworthy
QueryStory has emerged from stealth with $6 million in seed funding to tackle a problem plaguing AI chatbots and search tools: confidently wrong answers. The startup's core pitch is that large language models often produce responses that sound authoritative but fall apart under scrutiny, whether due to hallucination, contradictory sourcing, or logical gaps.
Rather than building another LLM, QueryStory applies techniques borrowed from cybersecurity, like anomaly detection and verification pipelines, to check AI outputs for internal consistency and factual grounding before they reach users. The idea is to act as a trust layer that sits between a model and the person relying on its answer.
The funding round signals investor appetite for infrastructure that addresses AI reliability rather than raw model capability, a growing subcategory as enterprises grow wary of deploying chatbots without safeguards.