Cutting manual policy review by ~80%
DocuSentinel AI turns mountains of incoming requests into instant verdicts — Approved, Denied, or Escalate — backed by precise policy citations and recommended actions. Edge cases go to humans. Everything else clears in seconds.
The problem
Departments in regulated environments receive thousands of daily requests — approvals, access changes, exceptions, procurement. Each requires a human reviewer to read the request, locate the right policy, interpret the rules, and issue a ruling. The bottleneck grows linearly with request volume; the cost grows with headcount; error rates creep up as reviewers get tired.
The team needed an engine that could handle the routine volume without a cloud dependency, without hallucinations, and with a fully auditable decision trail.
Our approach
We built DocuSentinel AI as a document-to-verdict pipeline with three non-negotiable properties:
- Grounded answers only. The LLM never reasons in isolation. Every verdict cites the specific policy clause it relied on — no citation, no verdict.
- Triage, not replacement. Clear-cut cases get Approved or Denied automatically. Ambiguous ones get Escalated to a human with a pre-written summary, saving reviewer time even on edge cases.
- Deployable offline. The whole stack — model weights, vector store, orchestration — can run inside a client’s network with zero external calls.
How it works
- Ingestion. Incoming requests (email, ticket, form) are normalized, redacted of PII not needed for the decision, and chunked.
- Policy retrieval. Active policy documents are loaded into a local vector index. The relevant clauses for each request are retrieved via hybrid search (BM25 + dense embeddings).
- Reasoning. An LLM produces a verdict + citation + recommended action, constrained to a strict output schema.
- Verdict routing. Approved/Denied flows auto-complete. Escalate flows route to the appropriate human queue with full context.
- Analytics. A dashboard tracks volume, escalation rate, and policy-citation frequency for continuous policy tuning.
Tech stack
Why it mattered
Reviewers went from drowning in routine triage to focusing on the 20% of cases that actually needed judgment. The citation requirement built trust with legal and compliance teams — every decision is defensible on demand, not a black-box ruling.