01The problem
Legal agents fail differently than sales agents. A wrong fact in a redline is not embarrassing — it is malpractice. The teams shipping AI into contract review, compliance, and discovery cannot use a memory layer that can't tell you which version of the clause it relied on, when it was ingested, and what contradicts it.
02What memory unlocks
- Versioned facts with validity windows so the agent never quotes a superseded clause.
- Source-grounded recall — every memory carries provenance back to the document, page, and paragraph.
- Contradiction surfacing with manual-review queues for high-stakes ambiguity.
- Full audit trail, retained 1 year on Scale and configurable on Enterprise. Required for E&O insurance and bar-association review.
03What it looks like
Legal is the vertical AgentPrizm's audit trail was designed around. An agent doing contract review has to defend every redline to a partner — so every clause-level recommendation surfaces its supporting memories: the original clause, the firm's standard position, prior counter-positions on this client, and any superseding precedent. Each memory carries provenance back to the document, page, and paragraph, and contradictions route to a manual-review queue instead of resolving silently.
04Illustrative impact
| Metric | Before | With AgentPrizm | Δ |
|---|---|---|---|
| First-pass review time | 4.2h | 2.6h | −38% |
| Partner-flagged factual errors | 4 / 100 docs | 0 / 100 docs | −100% |
| Audit prep time, monthly | 2 days | 2 hours | −92% |
| Reviewer trust score (qualitative) | "sometimes" | "always" | — |
Illustrative targets that model the mechanism above — not results from a named customer. We'll run an eval on your own data.
05Integrations
iManage, NetDocuments, Relativity, and your internal precedent DB. Container isolation and full audit trails support attorney-client-privilege workflows.
Wiring is the same regardless of vertical: ingest and recall over the memory API, or connect any MCP-capable agent with the five-minute quickstart.
See it on your data.We'll run a 2-week eval against your existing agent — same prompts, same model, only the memory layer changes. Start a pilot.