Factagora

Companies building chatbots, agents, and copilots on top of LLMs, vector databases, or RAG stacks face the risk of AI hallucination, where outputs are not grounded in verified facts. Today these teams rely on their existing LLM and retrieval stack without a dedicated verification layer, meaning claims generated by AI are not checked against original sources or scored for confidence. Legal and other high-stakes fields need trustworthy AI research tools; a case study describes a top Korean law firm needing to restructure 350,000 documents to make them verifiable, implying that unstructured document stores are not inherently checkable against source truth. Without a verification step, organizations risk deploying AI answers that lack citations or confidence verdicts, undermining trust in their AI services.

Factagora offers a fact verification API with three endpoints: Fact Search, which searches verified sources and returns ranked, cited results for a query; Fact Checker, which verifies a claim against original sources and returns a confidence-scored verdict (for example TRUE with confidence 0.94, citing sources like Reuters, Tesla's investor relations, and SEC filings); and Evidence Finder, which surfaces supporting and opposing evidence ranked by strength. The product is designed as a single API layer that sits between an application and its existing LLM, vector DB, or RAG stack, requiring no migration or retraining, just one API call to add a grounded answer with sources and a confidence score. Access uses bearer authentication with structured JSON responses and an included OpenAPI spec, and developers can try it in a free Playground without a credit card.