WtrDB for
Conversational AI
Voice agents and LLM assistants have solved how agents talk. WtrDB solves what they're allowed to know, claim, and prove — with a typed knowledge graph, a three-filter governance pipeline, sheaf-theoretic conflict detection, and cross-agent federation that makes enterprise knowledge hold up under scrutiny.
RAG finds chunks. WtrDB grounds facts.
Latency, voice quality, and tool use are no longer the moat. What enterprise customers actually complain about: the agent hallucinates facts about the business. It contradicts itself across channels. It can't explain why it said something when compliance asks. It can't be safely updated without risking regressions. Multiple agents — sales, support, billing — drift apart because each owns its own RAG store. These are not generation problems. They are knowledge problems.
Three steps from loose retrieval to governed agent knowledge.
Ingest and Extract
Documents, policies, product data, event streams, and agent-contributed facts enter a unified pipeline. WtrDB extracts a typed, temporal knowledge graph with provenance back to every source chunk. The agent stops paraphrasing PDFs and starts answering from structured facts.
Govern Every Fact
Every candidate fact passes three checks: Evidence (is it actually in a source document?), Logical (does it contradict what the graph already asserts?), and Evolutionary (is it a real update, or a stale claim resurfacing?). Contradicted facts are soft-deprecated — not deleted — so every assertion is auditable at call time.
Query, Monitor, and Federate
Agents query the graph in natural language and receive structured answers with cited evidence attached. The sheaf engine continuously measures internal consistency, flagging high-conflict regions before deployment. Federation aligns multiple agent knowledge bases into one governed operational view.
Explore and rotate your WtrDB graph in 3D
Drag to rotate. Scroll to zoom. Right-click and drag to pan. Click a node to inspect its relationships.
Six ways to give agents knowledge that holds up.
Grounded Agent Memory
Replace the blob of top-k chunks with a typed graph of entities, relations, and events. Hybrid retrieval is built in — when graph signal is weak, the system falls back to vector RAG automatically. No regression versus the current stack, only an upgrade path.
Cross-Agent Consistency
A sales agent, a support agent, an onboarding agent, and a billing agent — each drifting. WtrDB federation aligns those knowledge bases, tracks merge conflicts as explicit records, and gives the product team one view of what all agents currently believe. The company speaks with one voice across channels.
Compliance-Grade Auditability
For regulated verticals — healthcare, finance, insurance, legal — WtrDB produces the artifact compliance teams actually ask for: 'The agent said X because on date D, source S asserted X, and that assertion was current at call time.' Soft-deprecation history means the full knowledge timeline is always reconstructible.
Sheaf Conflict Detection
The sheaf engine computes a Laplacian over local graph neighborhoods. Nontrivial first cohomology surfaces as a measurable signal — a region where agents are likely to give inconsistent answers. Pre-deployment: 'you uploaded a new policy; here are the 12 entities that now conflict.' Runtime: route high-cohomology questions to a human or hedge the answer.
Safe Continuous Updates
Re-chunk, re-embed, redeploy, and pray nothing regressed — replaced. WtrDB's adjudication pipeline makes updates incremental, provenance-tagged, and rejected at the boundary if they violate logical or evidential constraints. Knowledge evolves the way a database evolves. For teams updating prices, policies, and scripts weekly, this is operationally enormous.
Drop-In Knowledge API
kg.query(question) returns a structured answer with cited evidence. kg.learn(fact, source) lets agents append newly-learned facts with provenance on live calls. The hybrid retrieval endpoint drops into existing RAG pipelines as a direct replacement. Same integration shape as a vector DB — graph reasoning and governance included.
Built for teams shipping enterprise conversational systems.
Voice Agent Platforms
A differentiated Enterprise Knowledge tier above commodity RAG, with compliance-grade auditability.
Realtime LLM Providers
A reference grounding layer for agentic deployments where hallucination cost is high.
Vertical AI
Evidence trails per utterance and conflict surfaces — the artifacts regulators actually ask for.
Enterprise Assistant Builders
Federated knowledge across teams without each team owning a divergent index.
Conversational Analytics & QA
A structured graph to evaluate agent answers against — 'did the agent say something the KB doesn't support?' becomes a computable check.
Inconsistency is now a number, not a guess.
WtrDB models local neighborhoods of the knowledge graph as a cellular sheaf and computes a sheaf Laplacian. Nontrivial first cohomology — H¹ — is treated as a measurable obstruction: a place the knowledge base is internally inconsistent. For conversational AI, this becomes a pre-deployment conflict check, a runtime routing signal, and a dashboard of regions where agents are likely to diverge. No vector store offers this. No retrieval framework computes it.
Ready to give your agents something real to know?
Talk to the team about your agent stack, knowledge sources, and deployment requirements.