Your AI features create real value, but you can't measure it, so you can't charge for it. We build the data infrastructure that connects AI actions to business outcomes, so you can move beyond seats and charge on value.
You've shipped AI features that genuinely help your customers. But internally, the economics don't add up and your pricing doesn't reflect the value you're creating.
We work in three phases. Each delivers standalone value, and together they build a complete attribution system your engineering team can execute on.
We audit every AI feature against its current ability to prove business impact, and map the telemetry gaps standing between you and outcome-based pricing.
We model the financial impact of each AI feature, assign attribution confidence scores, and design the pricing evolution roadmap.
We deliver technical specifications your engineering team can execute: event schemas, data pipelines, and pilot frameworks for testing AI impact.
Every AI feature sits on two axes: how independently it operates (autonomy), and how clearly its output links to a business outcome (attribution). Together, these determine your pricing power.
The goal is to move features toward the top-right quadrant, where AI acts independently and you can directly measure the revenue or cost impact. That's where value-based pricing becomes defensible.
Acts independently, but its impact is hard to isolate and measure. Pricing uses proxy signals like tasks completed.
Independent with clear, measurable business impact. Strongest pricing power and highest willingness to pay.
Human-driven, limited attribution. Pricing is tied to headcount, not value delivered.
Human-in-the-loop where usage correlates with measurable ROI. A stepping stone toward outcome pricing.
If your product has AI features and your pricing hasn't caught up, we can help regardless of your stage, vertical, or architecture.
AI coding assistants, test generation, or security remediation tools.
Agentic workflows that need outcome validation for enterprise pricing.
Industry-specific platforms adding AI but stuck monetising through seat licenses.
Services transitioning from compute-based to value-based pricing.
Traditional pricing consultants focus on competitive benchmarking and willingness-to-pay surveys. That's necessary but insufficient. The real blocker is technical: most SaaS platforms simply don't have the data infrastructure to prove what their AI delivers.
We bridge the gap because we work across three disciplines that rarely sit in the same room. The intersection of all three is where the real work happens.
If your AI features are priced like traditional software, you're leaving revenue on the table and watching margins erode. We'll help you fix that, starting with a conversation about where you are today.
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