For growth-stage companies
Multiple product lines, compounding operational drag.
By Series B and beyond the constraint moves. Deciding what to build is no longer the hard part. The hard parts are the operational load the product has quietly accumulated, the integrations holding the whole thing together, and the data you have been collecting for years without an agreed plan for what it is worth.
Growth-stage AI work fails for predictable reasons, and almost none of them are about models. It fails on data readiness, on integration constraints, and on workflows nobody mapped before automating them. The same is true of data monetization: the blocker is rarely buyer interest, it is consent language, organization, and the absence of anyone owning the decision.
Sound Familiar?
This is for you if
The Work
What changes.
Workflows mapped before anything gets automated
Where the real burden sits, which parts are automatable today, and which need upstream work first. Automating an unmapped workflow reliably makes it worse.
AI sequenced against what your data can support
A phased plan with the data and integration prerequisites named per phase, so engineering commits to something buildable rather than to a direction.
A data strategy with one agreed priority
Assets inventoried against real buyer criteria, near-term revenue paths separated from long-term asset building, and a decision made rather than deferred again.
Engagements
What usually fits at this stage
Lightest first. Starting small is a real option, not a lesser one.
1–2 Days / Week
Fractional CPO
Embedded product leadership one to two days a week, accountable to outcomes rather than to a deliverable.
Learn moreAudit + 6–12 Month Roadmap
AI & Workflow Modernization Audit
A structured read on what AI can genuinely do in your context, and a phased plan your engineers can build.
Learn moreWorking Session + Roadmap
Data Strategy & Monetization
A facilitated session and sequenced roadmap for turning proprietary health data into revenue.
Learn moreWhen this is the wrong page
If you are pre-Series A, this is heavier than you need. Start with the seed or Series A page.
FAQ
Questions at this stage
We already have a product org. Where does this fit?
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Usually as scoped work alongside your team rather than in place of it: an audit your team executes against, a facilitated strategy session that breaks a stalled decision, or embedded leadership for one product line. Displacing a functioning product org is not the goal.
How do you assess AI readiness without a long discovery phase?
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The audit runs three to five weeks with a two-week synthesis. It moves quickly because it is looking at specific things: data quality and completeness, API and integration maturity, where manual burden actually concentrates, and governance constraints. Those are findable fast when you know what to look for.
Is the data monetization work healthcare-specific?
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The deepest version is, and deliberately so. Health tech buyers, consent language, and the routes to revenue are specific enough that generic advice is close to useless. The underlying method transfers to other data-rich businesses, but health tech is where the pattern knowledge is genuinely differentiated.
Can you work with our existing vendors and consultants?
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Yes. At this stage there are usually incumbents in place and the useful contribution is often making the pieces cohere rather than replacing any of them.
More on scoping, pricing, and how engagements run on the full FAQ.
Other Stages
Not quite where you are?
Get started
Start with a conversation.
Thirty minutes, no deck. You describe what is stuck and the call ends with a recommendation, including when that recommendation is to wait.