Redact and embed
Sensitive values are redacted before the new narrative enters retrieval.
CASE STUDY · APPLIED AI / FULL STACK
A cited, human-in-the-loop RAG workflow for investigating financial complaints using historical precedent and operating policy.
Complaint teams may need to compare a new case against thousands of historical narratives while following internal handling procedures. A plausible model response is not enough when the facts are sensitive and the recommendation can affect a customer.
ComplaintOps brings the evidence together, but deliberately stops short of autonomous resolution. The model recommends; an employee reviews, edits, approves, or rejects.
The model can call retrieval tools for historical CFPB cases and demonstration operating policy. It never receives direct database access, and every recommendation returns structured fields and citations for inspection.
Sensitive values are redacted before the new narrative enters retrieval.
Historical complaints and operating policy remain distinct evidence sources.
Routing, priority, actions, confidence, and citations arrive in a typed response.
The operator can trace recommendations back to the retrieved passages.
A human approves, edits, or rejects rather than delegating the final action.
Review events and changes remain available for later inspection.
The project distinguishes retrieval benchmarks, end-to-end smoke tests, and live-agent checks. This makes failures easier to localize and keeps performance claims appropriately qualified.
ComplaintOps demonstrates the engineering work between an AI prototype and an operable product: ingestion, retrieval, structured outputs, evaluation, security, deployment, and a clear human checkpoint.