The DPO closes the gap between what you build and what it's worth, tying every pricing decision to the usage data you already trust, so monetization keeps pace with the roadmap instead of trailing behind it.
Try for FreeProduct teams treat every roadmap decision as evidence-based, except pricing. The DPO eliminates this blindspot to ensure that what ships and what it's worth move on the same timeline.
Pricing gets decided in a rush right before ship, disconnected from the roadmap work that created the value.
There's no clean line between what a feature is worth and what customers are willing to pay for it.
New value ships on the roadmap, but pricing takes months to catch up, if it ever does at all.
Structure packages so the middle tier is the one most customers choose, not an afterthought bolted onto the roadmap.
Test This Workflow →Use real feature adoption data to decide what moves tiers, what becomes an add-on, and what stays included.
Test This Workflow →Choose the unit of value your price should scale with to ensure that revenue and usage grow simultaneously.
Test This Workflow →We ran DPO through a structured, repeatable evaluation against general-purpose AI, scored by an expert evaluator persona calibrated to Chief Pricing Officer, VP, and Director standards, across seven professional dimensions, including trustworthiness, hallucination risk, and quantitative rigor.
That's the same rigor your product decisions already go through, now applied to pricing.
DPO Output · Packaging Decision
"Should the new AI feature be a standalone add-on or bundled into our top tier?"
Usage data shows only 18% of accounts would hit the threshold to justify a standalone SKU today. Recommended: bundle it into the top tier for now, track adoption, and revisit as a paid add-on once usage crosses 35% of the base.
Run your first workflow and see where your product is outpacing its cost.
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