Build auditable knowledge-graph reasoning for reproducible, contradiction-aware AI answers.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Predicate" yet — see the docs or source repo.
Import these research notes, web excerpts, and database records into Predicate as a provenance-aware knowledge graph. Detect contradictory triples, then answer: 'Does compound A improve metric B?' Include evidence sources and the reasoning path for each conclusion.
A conclusion with evidence chains, highlighted supporting and conflicting claims, and a reproducible reasoning trace.
Sync product docs, release notes, and support FAQs into Predicate so an AI assistant can answer: 'From which version is feature X supported, and what are its limitations?' Return the final answer, the relevant triples, each triple's source, and any contradiction notes.
An auditable product answer with sources, version evidence, and contradiction notes.
Use Predicate to record key facts, decisions, and dependencies in a team project. When new information arrives, update the knowledge graph while preserving provenance for prior conclusions. Then answer: 'What is the most likely root cause of the current system failure?' and explain how the conclusion changes with new evidence.
A root-cause analysis that evolves with evidence, showing prior basis, latest judgment, and contradiction handling.
Expose a read-only provenance-backed fact graph for AI agents to search and verify.
Trace multi-graph causal paths to answer why questions with confidence scores.
Provides an MCP server for RAG and knowledge graph integration.
Track structured reasoning with confidence, branches, and revisions for complex problem solving.
Preflight checks vague prompts and improves AI coding workflows with less token waste.
Secure internal knowledge retrieval with permission-aware access control and citation enforcement.