SaaS priced seats. AI prices outcomes. Revinci is the first revenue platform with a Value Engine, a Cost Engine, and a Margin Engine wired into the same pricing core.

Introducing: https://www.revinci.ai/

Every pricing leader I've talked to in the last 12 months is wrestling with the same four questions:

How do I quantify the value my AI agent actually delivers — and turn that into a billable unit?
How do I price it when my COGS is a moving target on every API call?
How do I model 20+ pricing variants — flat, tiered, token, outcome, hybrid — without CPQ and billing drifting out of sync?
How do I see my margin in real time, not eight weeks later when finance closes?

For 20 years, value-based pricing has been the holy grail no SaaS platform could operationalize, because SaaS value is invisible — you can't meter "the CRM helped close a deal." Agents are different. Every agent action is a measurable outcome event. Tickets resolved. Hours saved. Meetings booked. Bugs triaged. Documents drafted. For the first time, the value an AI delivers is instrumentable — and that means value-based pricing is finally executable.

Revinci is built around exactly that shift.

The three engines pricing pros should care about:

Revinci runs on a unified Sell + Bill platform, and the heart of it is three engines that move together on every event:

Value Engine — model what each agent creates for the customer. Hours saved, tickets resolved, meetings booked, manual-labor cost displaced, time-to-resolution compressed. Every outcome becomes a first-class, meterable, billable unit. This is the engine that turns "we save you 40% of agent handle time" from a sales claim into a contract clause.

SmartCost — real-time cost-to-serve per agent, workflow, customer, and model. Tokens, compute, storage, API calls. COGS visible as revenue is created.

SmartMargin — live gross margin per deal, per customer, per agent. Guardrails auto-flag below-floor transactions. Leakage detection. Profitability scorecards.

In pricing terms: value, cost, and margin are no longer three separate spreadsheets owned by three separate teams. They're one continuous signal, evaluated on every event, every quote, every invoice line.

I'd love to walk this community through reference models we've built — how to structure a "per ticket resolved" pricing unit end-to-end (value definition → meter → rating rule → wallet → guardrail → invoice line), how to build a hybrid "subscription + outcome bonus" model, and how to use the Value Engine to defend premium price points in deal negotiations. If anyone here is actively designing AI monetization, I'll bring the architectures, not a deck.

0 comments