Introducing: https://www.revinci.ai/ Every pricing leader I've talked to in the last 12 months is wrestling with the same four questions: 1. How do I quantify the value my AI agent actually delivers — and turn that into a billable unit? 2. How do I price it when my COGS is a moving target on every API call? 3. How do I model 20+ pricing variants — flat, tiered, token, outcome, hybrid — without CPQ and billing drifting out of sync? 4. 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’ve been thinking about how pricing is evolving in the context of AI agents, and I’d really value your perspective. Historically, pricing has often been a bottleneck in the sales process—but over time, we’ve built systems (CPQ, billing, usage-based models) that make pricing more structured and scalable. With AI agents, it feels like we’re facing a new version of that challenge—not in pricing execution, but in defining and measuring value. While costs (tokens, infra, etc.) are relatively transparent, the value created by agents is much harder to quantify consistently. We’re currently exploring ways to decode this as part of our product journey. I’d love your thoughts on a few open questions: 1. Defining value: What key parameters or signals should be captured to quantify the value created by an AI agent (e.g., time saved, revenue impact, quality improvements)? 2. Data accessibility: In your experience, how willing are customers to share internal metrics required to measure value? What has worked (or failed) in getting this data? 3. Human-in-the-loop (HITL): How should we think about attributing value when humans are partially involved in the workflow? This seems like one of the hardest aspects to standardize. 4. Customer success criteria: How do you recommend aligning pricing with what “success” actually means to the customer, given that this can vary widely? 5. Product vs. sales responsibility: Should value measurement and tracking be embedded within the agent/product itself, or handled externally in the sales/pricing layer (e.g., CPQ, analytics tools)? If you’ve seen strong frameworks, patterns, or even failed approaches in this space, I’d be very interested to learn from them. Thanks in advance for your time—I’d really appreciate any guidance you can share.

I've been thinking about pricing in the context of AI-driven quoting which leads to highly dynamic pricing. Let's say we get really good at coming up with prices that maximize conversion (AI + sales). But what happens once the customer commits and we start charging on a regular basis? That's where I see a gap. In most setups I've seen, we lose the context behind the price very early on: - why is this customer paying $X instead of $Y? - was it an AI suggestion, a discount, or a manual override? - what inputs influenced the price point? I'm thinking about this problem from the perspective of plan migrations, where we often don't know how to handle certain (small) cohorts with non-standard billing setup. In your experience, is it a common practice to link pricing decisions (CRM / quoting + context) to the actual billing objects (subscriptions, customer records, etc.) in a structured and automated way?

Where in your opinion SaaS companies mostly lose money? My hypothesis is that revenue is rarely lost in big events. More often than not, the loss happens over time due to small, repeated inconsistencies across systems. What I've seen: - deals don't fully match billing / legacy deals "we don't touch" - discounts don't expire - usage isn't fully captured - product evolves, but billing lags behind - leaving premium feature up for grabs None of these are dramatic, but they eventually accumulate to a small percentage of ARR. Does this match your experience? PS: Productivity hit is a separate topic: reporting in spreadsheets trying to merge edge cases and data from various systems.

Have been thinking a lot on this as a CEO and CFO(of a AI product selling company) or just a account sales head what you like to see? when it comes to credit based pricing both kind included Credit and credit as currency. Especially when there are credits at the feature level. What would you like to see from a dashboad'ing perspective?