We built an AI-native CPQ and billing platform that unifies pricing, quoting, usage-based billing, invoicing and revenue recognition in one engine — eliminating the revenue leakage that happens when sales and finance operate in separate tools.
The platform meters down to individual tokens and API calls, making it a revenue engine that can handle AI and SaaS usage-based pricing with the precision those models require.
Pricing lived in spreadsheets. Billing ran on a separate tool. Every time pricing changed, someone had to manually update both — and the gaps between them were where revenue disappeared. For AI companies with token-based and API-call-based pricing, the problem was worse: no legacy billing tool had the metering granularity to handle it.
The result was systematic revenue leakage that showed up only at reconciliation — by which point it was too late to recover.
Pricing rules, discount logic, approval workflows and quote generation all run from a single engine — so what sales quotes is always what finance will bill.
Usage-based billing metered down to individual tokens and API calls — the granularity required for AI products, SaaS consumption models and any usage-tied pricing.
Every pricing change propagates across the full quote-to-cash chain in real time — no manual updates, no synchronization lag, no reconciliation gaps.
Native multi-currency support with automatic revenue recognition rules — GAAP-aligned, audit-ready, without a separate accounting integration.
Connects into existing CRM and ERP systems so sales and finance work in their own tools — with the billing engine handling synchronization automatically.
Revenue Leakage
Unified quote-to-cash engine eliminates the gaps between sales pricing and finance billing — systematically, not on a case-by-case basis.
Pricing Propagation
Every pricing change reflected across quotes, invoices and revenue recognition simultaneously — with no manual steps.
Usage Metering
Granularity down to individual tokens and API calls — making AI and consumption-based pricing accurate for the first time.