CASE STUDY · APPLIED AI / FULL STACK

ComplaintOps

A cited, human-in-the-loop RAG workflow for investigating financial complaints using historical precedent and operating policy.

ROLE
Product + full-stack engineer
CONTEXT
Personal RAG learning project
STACK
React · FastAPI · PostgreSQL · pgvector
DEPLOYMENT
Docker · Render · Neon · Auth0
01 · THE PROBLEM

Support decisions need precedent, policy, and judgment—not another opaque answer.

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.

02 · THE SYSTEM

Two evidence channels. One controlled agent. A human decision.

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.

01 · PREPARE

Redact and embed

Sensitive values are redacted before the new narrative enters retrieval.

02 · RETRIEVE

Search separately

Historical complaints and operating policy remain distinct evidence sources.

03 · RECOMMEND

Return structure

Routing, priority, actions, confidence, and citations arrive in a typed response.

04 · REVIEW

Inspect evidence

The operator can trace recommendations back to the retrieved passages.

05 · DECIDE

Keep accountability

A human approves, edits, or rejects rather than delegating the final action.

06 · AUDIT

Preserve history

Review events and changes remain available for later inspection.

03 · EVALUATION

Evaluate retrieval and workflow separately.

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.

COMPLAINT PRECISION@586.0%PROVISIONAL BENCHMARK
COMPLAINT nDCG@50.818PROVISIONAL BENCHMARK
POLICY HIT@386.7%PROVISIONAL BENCHMARK
POLICY MRR@371.7%PROVISIONAL BENCHMARK

A RAG project designed around evidence and human judgment.

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.