Case study · revenue assurance · verified replay
Revenue leaks hide between systems. Reconcile before action.
Marketing Revenue Assurance compares what ad platforms report, what the CRM received, how demand moved through the funnel, and what finance collected. It explains the gap, ranks the exposure, and stops before any external mutation.
Marketing Revenue Assurance demonstrates how bounded agent collaboration and deterministic controls can identify cross-system revenue leakage without giving a language model authority over arithmetic, access policy, or client systems.
Live interface
Inspect the replay before the architecture.
The screen below is the current public system using synthetic scenario data. It does not expose client production records or perform an external write.
Marketing Revenue Assurance
Advertising, CRM, funnel, and settlement evidence reconcile into one reviewable revenue risk. No external write is performed.
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Case-study figures describe this documented engagement and are not forecasts or guarantees.
The operating constraint
A marketing team can report delivered leads while the CRM shows missing records, booked pipeline exceeds collected cash, attribution coverage collapses, or stale exports hide the current state. Looking at one platform at a time makes the leak difficult to see and easy to explain incorrectly.
The system Aixcel designed
Aixcel built a typed FastAPI control plane and a 12-node LangGraph workflow with ten bounded specialist responsibilities. Deterministic Python services validate source contracts, content-hash evidence, reconcile delivery and cash, calculate funnel benchmarks and risk, enforce role access and quotas, and require a signed human decision. PostgreSQL, Alembic, durable audit records, checkpoints, OpenTelemetry, Prometheus, structured logs, evaluation fixtures, and replay complete the production path.
The documented result
The public system exposes 12 materially different scenarios across Meta Ads, Google Ads, TikTok Ads, GoHighLevel CRM, call tracking, and settlement exports. The cash collection case identifies a $44,700 booked-to-collected gap and proposes a draft recovery plan. The stale-source and multi-failure cases fail closed. Approval and rejection work across isolated serverless instances through a signed receipt, while Docker and PostgreSQL preserve durable runs, hash-chained audit events, and LangGraph checkpoints. The release passed 31 tests, 81.93 percent measured coverage, 25 Postman assertions, 13 golden evaluation metrics, two Docker and PostgreSQL CI paths, and public browser journeys.
System components
Python 3.12, FastAPI, Pydantic v2, LangGraph, SQLAlchemy, PostgreSQL 17, Alembic, REST, OpenAPI, Postman, OpenTelemetry, Prometheus, Docker, GitHub Actions, Playwright, Vercel
How to interpret this evidence.
Names and sensitive details are withheld. Metrics retain their stated meaning and evidence label. A scope count is not converted into an outcome, and no engagement result is presented as a universal benchmark.
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