The Practice - Technical leadership for health tech, where the clinical judgment and the code are the same judgment.
Fractional CTO/CPO leadership and hands-on execution for health-tech companies — from a pre-seed idea that needs building, to a later-stage team making a bet it doesn’t have the capacity to run alone.
This is the half of Flanders Forest you hire. If you’re looking for a co-founder rather than a firm, that’s the Studio.
Who this is for - Health-tech companies, pre-seed to Series C
Two patterns show up most often. Both are welcome — most engagements fall clearly into one or the other.
Pre-seed & seed founders
You raised on an idea and now need to actually build it. Most of our engagements start here — as the technical co-founder a founder needs before they need, or can afford, a full-time exec.
Later-stage teams, up to Series C
You’re making a targeted technical bet — a new product line, a regulated-AI feature, an integration you can’t get wrong — but don’t currently have the in-house capacity to run it yourselves.
Founders without a technical co-founder
You have the idea and the domain insight but no one to build it, and a retainer isn’t really what you’re looking for. That’s a studio conversation, not a services one — we co-found for equity, and sometimes bring capital too.
Best fit
How we work - Five ways to engage
Standard engagements run a two-month minimum and extend for as long as they’re useful — most run around twelve months. The audit and de-risking engagements are scoped to their question instead, and often run in days.
- Fractional CTO / CPO Retainer. Embedded, part-time technical and product leadership — roadmap, architecture, hiring, and the calls only a senior technical exec can make, without the cost of a full-time one.
- 0→1 Build Engagement. Dedicated, hands-on execution to ship a real first version. Working code, not advice from the sidelines — the same standard the case studies were built to.
- AI Operations Audit. A short diagnostic of a real operational process, ending in a ranked map of automation candidates scored on volume, cost of a wrong output, PHI exposure, and reversibility — so you know which 20% is safely automatable before anyone commits to building it.
- Technical Audit / De-risking Spike. A short, tightly scoped engagement to answer the one question a bigger bet depends on, before a team commits its budget to it. Scoped to the question — days to a few weeks, independent of the standard minimum below.
- Investor / Board Technical Advisory. Technical diligence for a board or investor evaluating a health-tech bet — architecture, regulatory exposure, and whether the team and plan match the claim.
What I build - Seven domains, each proven on a real system
Every domain below links to a real case study — the architecture, the trade-offs, and the reasoning are documented, not asserted.
AI Operations
Deciding which parts of an operational process an AI can safely take over — and which will quietly produce errors nobody catches for months. The scarce skill is not wiring a model to an inbox; it is scoring each step on volume, cost of a wrong output, PHI exposure, and reversibility, then automating only what survives.
Regulated AI Systems
Building AI into clinical products where the model proposes but never decides the medically consequential part — pathway logic, publish gates, and retrieval scoped so a mistake is structurally prevented, not just discouraged.
Compliance-Grade Architecture
HIPAA-scoped systems designed so a compliance failure’s blast radius is small by construction — account isolation, data boundaries, and integration strategy sequenced to the regulatory reality, not around it.
Deterministic, Audit-Grade Systems
Billing- and regulation-critical logic that’s pure, replayable, and defensible under audit — a system that blocks an uncertain outcome rather than guess in anyone’s favor.
See it in practice
0→1 Execution & Vendor Orchestration
Shipping the first real version across multiple vendors and systems with no shared trust boundary between them — and the design-system foundation that keeps it from being rebuilt for the next product.
Technical Risk De-risking
Answering the one unproven assumption a product depends on — in days, not months — before engineering budget gets committed to an architecture built on a guess.
See it in practice
Mobile Product Engineering
Shipping real, installable mobile products — several React Native apps live on the App Store today — plus native iOS engineering (on-device audio capture, wake-word detection) validated end-to-end in the case study below.
See it in practice