Home / Search ends at an answer. CVS turns every answer into knowledge.
Living Knowledge

Search ends at an answer. CVS turns every answer into knowledge.

A living knowledge base improves itself with use. CVS captures expert answers, detects contradictions in a temporal knowledge graph, and retires stale facts without ever deleting history — adding 30–50 verified entries per week automatically.

Channels

Three input channels feed one living knowledge base.

Knowledge enters CVS three ways at once. Experts add facts manually through the web UI. The agent escalation loop captures answers to questions the base could not previously handle. And diff-reindex automatically re-ingests documents when they change, so updates flow in without a full rebuild.

Every channel converges on the same disciplined path: raw input becomes atomic facts, each fact is stamped with provenance, a contradiction check runs against existing knowledge, and a non-destructive patch lands in the living knowledge base. Nothing is overwritten, and everything is attributable.

  • Manual expert entry through the web UI for authoritative, curated facts
  • Agent escalation loop captures answers to previously unanswered questions
  • Diff-reindex re-ingests only what changed when documents are updated
  • All channels: atomic facts → provenance → contradiction check → non-destructive patch — about 30–50 new entries per week
Three input channels feed one living knowledge base.. Knowledge enters CVS three ways at once. Experts add facts manually through the web UI. The agent escalation loop captures answers to questions the base could not previously handle. And diff-reindex automatically re-ingests documents when they change, so updates flow in without a full rebuild.
Contradiction

Contradiction detection on a temporal knowledge graph.

When a new fact arrives, CVS does not blindly append it. The old fact and the new fact — each carrying source provenance and a validity period — enter the temporal knowledge graph, where the engine reasons about how they relate over time rather than just whether their text overlaps.

The check resolves to one of five outcomes: CONFIRMS, PATCHES, SUPERSEDES, CONTRADICTS, or NEEDS HUMAN REVIEW. Outdated knowledge is retired from retrieval the moment it is superseded — but its history is preserved, so you can still query what the base believed on any past date.

  • Old and new facts compared with full source provenance and validity windows
  • Five outcomes: CONFIRMS, PATCHES, SUPERSEDES, CONTRADICTS, NEEDS HUMAN REVIEW
  • Superseded facts drop out of retrieval automatically — no manual cleanup
  • History is never deleted: query the knowledge base as of any point in time
Contradiction detection on a temporal knowledge graph.. When a new fact arrives, CVS does not blindly append it. The old fact and the new fact — each carrying source provenance and a validity period — enter the temporal knowledge graph, where the engine reasons about how they relate over time rather than just whether their text overlaps.
Patches

Chunk patches and version chains preserve every original.

CVS updates knowledge at the fragment level, not the document level. A chunk evolves along an explicit version chain — Document v1 → chunk A → patch A1 → patch A2 → superseded by Document v2 — with typed edges recording exactly how each step derives from the last.

Because patches are non-destructive, original content is never rewritten. Retrieval always reads the current valid chain, while auditors can walk DERIVED_FROM, PATCHED_BY, and SUPERSEDED_BY edges to reconstruct the full lineage. This is what makes CVS defensible under SOX and similar version-control requirements.

  • Fragment-level patches — document updates never trigger a full reindex
  • Typed edges: DERIVED_FROM, PATCHED_BY, SUPERSEDED_BY, VALID_FROM, VALID_TO
  • Originals are immutable; retrieval always uses the current valid chain
  • Point-in-time lineage for audit, SOX, and regulatory version control
Chunk patches and version chains preserve every original.. CVS updates knowledge at the fragment level, not the document level. A chunk evolves along an explicit version chain — Document v1 → chunk A → patch A1 → patch A2 → superseded by Document v2 — with typed edges recording exactly how each step derives from the last.
Escalation

The agent escalation loop closes the gap in six steps.

This is the loop almost no enterprise AI closes. An employee asks; the five retrievers search; confidence falls below threshold; the question routes to the right expert; the expert's reply becomes atomic facts; and the next answer is instant and better. The loop is operational, not a thumbs-up button on a chat reply.

Routing flows through Slack, Teams, or the CVS escalation queue, so experts answer where they already work. Each captured answer is parsed into provenance-stamped facts and patched into the base, compounding into roughly 30–50 new knowledge entries every week — knowledge that stays even when the person who knew it leaves.

  • Six steps: ask → 5 retrievers search → low confidence → expert routed → atomic facts → instant next answer
  • Escalation via Slack, Teams, or the CVS queue — experts answer in their existing tools
  • Expert replies become provenance-stamped atomic facts, not lost chat history
  • Roughly 30–50 new verified entries per week, captured automatically
The agent escalation loop closes the gap in six steps.. This is the loop almost no enterprise AI closes. An employee asks; the five retrievers search; confidence falls below threshold; the question routes to the right expert; the expert's reply becomes atomic facts; and the next answer is instant and better. The loop is operational, not a thumbs-up button on a chat reply.

Watch your knowledge base teach itself.

In a 30-minute pilot we will escalate a real unanswered question, capture the expert reply, and show the contradiction check and version chain update live.

Start a Pilot