Management Cortex
Chat with Every Policy Document — Citation-Grade, Operator-Confident.
Your front-line team queries policy documents in natural language. Answers cite the clause. No RAG chunking. Built for operational accuracy, not enterprise search.
What it solves
In every service operation where decisions depend on contracts, policies, or product documents — insurance, healthcare, professional services, regulated industries — the same pattern shows up: a front-line operator needs to know what a 60-page document says about a specific case, and the document is the only authority.
Reading the document end-to-end on every query is impractical. Asking a senior person every time creates a bottleneck. Searching with Ctrl-F produces literal matches that miss the cross-references.
The shortcut — a generic chatbot on the document — uses RAG chunking, which fragments the text in ways that break exactly the cross-references and tables policies are full of. The chatbot confidently produces wrong answers because it's reading half the relevant context.
How It Works
The Cortex maintains policy documents in extracted, queryable form. Per-client or per-product, structured by carrier and document type.
When an operator asks a question, the Cortex loads the relevant document in full — not chunked, not fragmented. At Opus-class context windows, 100–300 pages of policy content fits in a single query.
The answer cites the clause: "Covered under Section 4.2(b), subject to the $500 deductible and 30-day waiting period in Section 3.7."
Two-stage retrieval: first checks the validated FAQ for a previously-confirmed answer; if not, falls back to full-document loading. The operator can promote any answer to the FAQ.
Cost per query is ~$1–2 against a full policy document. For a decision affecting a real claim or customer outcome, that's nothing.
The architectural call: full-document loading beats RAG at mid-market scale. RAG wins above ~1–2M tokens of corpus per query scope (enterprise legal discovery). Service-company operations live well below that threshold — full-load is feasible and better.
What You Get
- Per-client / per-product document ingestion and extraction pipeline (PDF → structured markdown)
- Full-document query interface with citation-grade answers
- Two-stage retrieval (FAQ first, full-document fallback) for cost efficiency over time
- Operator FAQ promotion workflow — turn validated answers into cached references
- Citation linking back to the source document and section
- Per-tenant deployment — your documents, your data, on infrastructure you control
In practice
A travel-health-assistance operation receives a group travel insurance policy — 87 pages, dense, with tables of covered benefits, exclusions, and waiting periods. A case examiner faces a question: a 67-year-old policyholder hospitalized 4 days into a trip; is the hospitalization covered, and what's the limit? With the Policy & Product Explorer, the examiner asks in plain English. The Cortex loads the full policy and composes: "Yes — covered under Section 4.2 (Emergency Hospital Care), subject to the $5M lifetime limit. Pre-existing condition exclusion (Section 5.3) does not apply because the policy was stable-and-controlled at issue, per the policyholder's submitted medical declaration." Citation is precise enough to act on. The decision happens in 90 seconds instead of 30 minutes spent thumbing through the PDF.