RevOps AI: Multi-Agent Pods for Healthcare RCM Auditing
A FastAPI + React B2B SaaS that combines autonomous agent pods, heuristic RCM data auditing, scikit-learn payment forecasting, and Monday.com board sync — engineered as collaborating CEO, Engineering, ML, and DevOps pods.
Revenue ops fails on hygiene before prediction
Healthcare billing teams often ask for forecasting first. In practice, missing fields and inconsistent records destroy both audits and models. RevOps AI treats data hygiene as the primary product surface: ingest RCM rows, flag defects, then forecast payment timing on what survives.
The platform is explicitly agentic in how it was built and operated — specialised pods collaborate on delivery — and operationally agentic in how audits and forecasts are orchestrated behind a JWT-secured API.
Agent pods as an operating model
The README describes an autonomous agile lifecycle: a PM agent shapes the blueprint, an Engineering pod ships FastAPI services and React surfaces, a Data/ML pod owns features and model persistence, DevOps/QA validates with pytest and GCP deploy, and a Marketing pod activates post-ship content.
Whether or not every pod is a literal LLM loop, the architecture forces modular ownership. That is the useful lesson for multi-agent product work: specialise interfaces, not just prompts.
Pod responsibilities
| Pod | Owns | Primary artefacts |
|---|---|---|
| CEO / PM | Roadmap and prioritisation | Blueprints, status board |
| Engineering | API + UI modules | FastAPI routes, React views |
| Data / ML | Features + models | Training jobs, persisted estimators |
| DevOps / QA | Stability + deploy | Pytest, Cloud Build, Docker |
| Marketing | Post-deploy narrative | SEO / sales sequences |
Automated auditing before cash-flow stories
The auditor scans ingested records for missing fields, formatting defects, and billing inconsistencies, then routes rows to manual review or auto-approval via heuristic rules. That split is deliberate: not every defect needs a model, and not every model should touch dirty input.
Monday.com integration pulls operational boards into the same engine so teams stop exporting CSVs as a weekly religion. Sync is a service module, not a side script.
Prediction on unaudited RCM extracts is a spreadsheet with confidence intervals. Hygiene first is the only honest sequencing.
Forecasting sits on top of the audit lane
Payment timing models use classical scikit-learn regressors — linear baselines and random forests — trained on historical billing features once records clear hygiene gates. Controllers get a cash-flow view that is only as trustworthy as the audit filter above it.
This is the same framing lesson as the healthcare payment-propensity paper elsewhere on this site: choose an actionable target, then pick a model family that matches the error that operators feel.
Schematic work mix on an RCM batch
Invented mix to show intent: most volume should clear heuristics; humans concentrate on the ambiguous middle; blocked rows never reach forecasting.
Source — Illustrative schematic — not client audit telemetry
Shipping shape
Docker Compose keeps API and frontend coherent locally; pytest gates Cloud Build; GCP hosts the production topology. None of that is exotic — it is what makes an agentic build story survive contact with real users.
The durable design bet is the combination: CRM sync + heuristic audit + classical ML + pod-visible operations. Remove any one and the platform collapses back into a dashboard demo.