RevQL
RevQL is a revenue recognition workbench for finance teams managing heterogeneous contracts without undergoing an ERP replacement. It pairs an open-source deterministic engine with managed deployment and data engineering pipelines to extract, review, and sync accounting schedules directly to existing general ledgers.
The Problem
Mid-market companies with complex sales motions frequently outgrow simple subscription-billing schedules long before they can justify a multi-quarter ERP overhaul. Signed order forms, custom master service agreements, professional services addenda, and hybrid consumption terms routinely arrive across disparate channels.
Enterprise revenue recognition modules require extensive technical infrastructure, specialized administrative staff, and rigid migration paths. Conversely, entry-level accounting tools assume uniform billing rows and clean tabular data. When contracts vary in structure, the burden defaults to manual spreadsheet schedules, leaving finance teams vulnerable to error during audit cycles.
Our Approach
RevQL establishes a review-driven pipeline between incoming legal documents and downstream accounting ledgers. The system operates on three foundational principles:
- Traceable extraction: Incoming contracts are parsed against a structured schema, returning exact character-offset citations for each extracted performance obligation, renewal clause, and payment schedule.
- Accountant verification: Extraction output is routed to an exception-based checklist. Every schedule, allocation, and modification requires explicit operator review prior to batch approval.
- Deterministic calculation: Amortization math, relative standalone selling price allocations, and journal entry calculations execute via audited, deterministic Python code rather than generative inference.
contract_doc (PDF, email, order form) --> schema extraction with character offsets --> human-in-the-loop review workbench --> deterministic ASC 606 calculation engine --> export to QuickBooks, Xero, or NetSuite
Architecture and Deployment
The core deterministic engine and schema extraction harness are open source, allowing teams to inspect calculation logic, self-host on-premises, or run workloads within private network perimeters.
For production operating environments, RevQL provides hosted multi-tenant workspaces alongside code-first data pipeline services. We build and maintain custom extraction and loading jobs using standard tooling to extract contract metadata from Salesforce CPQ, Stripe, and billing databases, mapping heterogeneous sources into a single versioned schema spine.
| Component | Implementation | Function |
|---|---|---|
| Core Engine | Open source (Python) | ASC 606 / IFRS 15 allocation, schedules, and journal entries |
| Extraction Tier | Local or dedicated inference | Span extraction and document offset mapping |
| Review Queue | Web application | Checklist interface for line-item confirmation and audit tracking |
| Data Pipelines | dlt, dbt, Dagster | Ingestion from CRM, billing, and order systems into the schema spine |
Status
We are (well, I am) currently assembling validation suites against commercial contract datasets and building out standard schema definitions for recurring and consumption-based software agreements.
Inquiries regarding design partnerships or self-hosted deployment architecture can be directed to chris@revql.com.
