Use cases
Concrete deployment patterns for grounding LLM applications with signed, sourced claims. Each use case bundles problem statement, integration pattern, code snippet, and expected outcomes.
AI agent grounding
Stop agents from hallucinating release dates, parameter counts, and architectural facts in multi-step tool-using chains. Drop verify_claim into the agent's tool catalog.
For: Agent developers using LangChain/LlamaIndex/OpenAI tools
RAG pipeline verification
Add a verification layer to existing RAG. Retrieval pulls the right doc but the model still emits wrong numbers; verify-then-respond catches the gap.
For: Teams running production RAG with hallucination tickets
Research citation tooling
Programmatic citations for academic + research AI tools. Stable claim IDs, primary sources with verbatim excerpts, HMAC signatures for reproducibility.
For: Research labs, academic AI projects, citation-required builds
Customer-support chatbot grounding
Stop bots from hallucinating product pricing, integrations, rate limits, AI/ML facts. Two-catalog pattern (your own product facts + SourceScore VERITAS) with route-to-human on unverified claims.
For: SaaS support teams, product chatbot builders
Content moderation — fact-check LLM outputs
Pre-publish verification gate for newsletter generators, blog assistants, report drafters. Extract atomic claims, verify each, flag or strip unverified before publish.
For: Editorial AI tools, content-generation platforms, marketing automation
Need a use case that isn't listed? Tell us — the next published use case is whichever pattern gets the most requests this month.