Revenue trust, metric clarity, safe automation
We make your revenue and customer data AI-ready by reconciling Stripe, CRM, accounting, and BI metrics before you automate workflows or launch copilots.
The commercial problem
Growing companies want AI agents, self-serve analytics, and automated reporting. The blocker is usually more basic: MRR, churn, active customers, refunds, disputes, and pipeline numbers do not mean the same thing across systems.
The fix is sequential: clean the data, define the metrics, automate the workflow, then monitor the AI.
Payments, refunds, failed charges, disputes, and accounting variance.
MRR, ARR, churn, active customers, CAC, LTV, and retention logic.
Owners, retries, logs, alerts, handoffs, and exception handling.
Use cases ranked by data quality, risk, observability, and control.
Productized suite
Start with a diagnostic, earn the implementation, then stay embedded through reporting, automation, and monitoring.
Find where Stripe, CRM, accounting, and BI disagree. Leave with a metric dictionary, source map, issue register, and fix roadmap.
Build the trusted layer for MRR, churn, active customers, refunds, cancellations, and discrepancy alerts.
Convert repeated reporting, CRM, intake, and finance workflows into monitored automations with logs, retries, and ownership.
Operating model
Map systems, definitions, dashboard logic, and workflow failure points.
Reconcile revenue, define metrics, and create trusted source rules.
Ship reliable workflows with logging, retries, and clear owners.
Track discrepancies, stale data, broken workflows, and AI handoffs.
Add self-serve analytics and copilots only after trust exists.
Quick diagnostic
Six questions across the same dimensions we audit. Not a replacement for the full assessment — but enough to show where your revenue data stands.
Commercial packaging
From $3,000 USD
Fixed-scope diagnostic for founder-led and RevOps teams.
Book auditFrom $12,000 USD
Dashboard, reconciliation logic, metric dictionary, and alerting.
Scope buildFrom $2,000/mo USD
Metric drift, workflow health, discrepancy alerts, and AI readiness.
Discuss retainerFinal pricing reflects scope, systems involved, and complexity. International clients welcome.
What clients receive
Who's behind this
I've spent 4+ years inside the exact data problems this service fixes — reconciling financial metrics at Allianz ($1.25B in capital instruments), building the retention and LTV/CAC dashboards at Origin Protocol ($150m in product launches), and designing financial infrastructure at Smith + Crown ($100m capital raise).
But more than consulting experience, I've built the tools: data quality audit engines that flag rule failures and anomalies across financial operations. AI-assisted data cleaning agents that profile, plan, and execute deterministic fixes. Governed decision-support systems for client coverage, churn risk, and retention. Production data warehouses with dbt, Airflow, and SQL pipelines feeding dashboards that operators actually trust.
Revenue Trust Layer exists because I kept building these systems from scratch for each engagement. The audit, the reconciliation logic, the metric dictionary, the monitoring layer — it's always the same work. Now it's a productized service.
Buying questions
No. The audit can start from exports, Stripe, HubSpot, QuickBooks/Xero, spreadsheets, and BI access.
Not first. The goal is to create trusted data, definitions, workflows, and monitoring so AI can be added safely.
Founders, CFOs, RevOps, Heads of Ops, and finance leaders who own reporting quality and operating cadence.
Power BI, Fabric, dbt, Python, n8n, HubSpot, Stripe, QuickBooks, Snowflake, BigQuery, and simple alerting stacks.
Start with the wedge
Send a short note with your stack, reporting pain, and which numbers are currently disputed. We'll reply within one business day.