Use cases
Concrete deployment patterns for adding bounded catalog retrieval and evidence review to LLM applications. Each use case includes limits as well as an implementation sketch.
AI agent grounding
Retrieve candidate catalog evidence for AI/ML assertions in tool-using chains, then compare the statement and sources before use.
For: Agent developers using LangChain/LlamaIndex/OpenAI tools
RAG pipeline verification
Add a candidate-record lookup to existing RAG, followed by an explicit evidence or entailment check before responding.
For: Teams running production RAG with hallucination tickets
Research citation tooling
Programmatic citation candidates for research AI tools, with stable claim IDs, cited evidence, and explicit review before formal citation.
For: Research labs, academic AI projects, citation-required builds
Customer-support chatbot grounding
Use your own authoritative product-facts catalog plus SourceScore candidates for bounded AI/ML facts, routing unsupported assertions to a human.
For: SaaS support teams, product chatbot builders
Content moderation — fact-check LLM outputs
Pre-publish evidence-review queue for generated drafts. Extract assertions, retrieve possible records, and require entailment or human review.
For: Editorial AI tools, content-generation platforms, marketing automation
News fact-checking — AI-assisted evidence review
A bounded AI/ML catalog can supply candidate evidence for editorial review; it is not a live-news source or automatic publish gate.
For: Newsroom tech teams, journalism AI tooling, fact-check organizations
Developer copilot grounding — stop coding assistants hallucinating libraries
Pair authoritative package and documentation checks with SourceScore candidates for the bounded AI/ML facts a coding assistant emits.
For: AI coding-tool teams, IDE extension builders, AI-pair-programmer products
Need a use case that isn't listed? Tell us — the next published use case is whichever pattern gets the most requests this month.