We help digital health and health tech companies run the commercial side of an AI-enabled product: buyer research, security evidence, onboarding and customer outcomes. Product and clinical safety decisions stay with your team.
AI helps a digital health company sell and support its product: drafting security questionnaire answers from an approved library, researching buying committees, answering onboarding questions and preparing renewal evidence. It can also make a product easier for customers' own agents to use through scoped connectors. Adding AI features to the product itself is a separate decision with its own safety and regulatory limits.
Clinical, operations, finance, IT security and compliance leaders each hold a veto, and intent is not deployment. NHS England reported 139 trusts live on its Federated Data Platform at the end of May 2026 against 170 signed up.
NHS-market products need a DTAC assessment, and organizations accessing NHS patient data complete the Data Security and Protection Toolkit yearly. US buyers want HIPAA evidence and business associate agreements. Stale answers slow deals.
McKinsey's late-2025 survey of 150 US leaders found health services and technology firms leading on generative AI implementation, with payers below 50%. That says nothing about how many providers will buy from you, and 36% of those firms' leaders said they build in-house.
EHR and cloud vendors are adding assistants and agent builders, and several list partner-built apps in marketplaces. A listing is distribution, not evidence of clinical effectiveness.
Integration was the most-cited barrier to scaling in the same survey. Each customer's EHR and data feeds differ, so implementation can stall for reasons outside your product and still show up as churn.
Established means widely deployed and well documented. Emerging means real deployments exist but results vary with your data and workflow. Early means pilots or research, not a safe default.
A maintained library of approved answers lets AI draft responses with a source and last-reviewed date for each. The real risk is a stale answer, so ownership of the library matters more than the tool.
AI summarizes public board papers, procurement notices and annual reports into a brief naming likely committee roles and concerns. It can misattribute roles, so the account executive verifies first.
AI answers administrators' how-to questions from your documentation, tracks implementation tasks and drafts status updates, handing anything about patient data or incidents to a person.
AI drafts a quarterly review from usage data and the customer's own measures, such as no-show rate or documentation time. The customer success manager checks every figure, because outcome claims must be traceable.
A scoped connector, such as an MCP server with limited permissions over FHIR resources, lets a customer's authorized assistant read specific data. MCP is established, but healthcare use is emerging, and no connector should expose a whole record by default.
The A2A protocol proposes a way for independently operated agents to coordinate. We found no verified routine clinical workflows using it across EHRs, and it does not replace patient authorization. Worth prototyping, not worth a roadmap.
Owns the narrative and decides which claims about outcomes, security and AI reach buyers and investors.
Signs off every security answer and decides what data may enter any AI tool, supported by AI governance work.
Decides whether a new AI feature is a device function and owns the safety case for NHS customers.
Own the champion relationships and the deal. AI prepares them but does not choose the approach.
Owns outcome reporting, tells customers what the data shows and escalates incidents.
Sourced draft answers and a ready evidence pack reduce back-and-forth, visible in review cycle time per deal.
Onboarding checklists and tracked tasks cut surprises between signature and first use, visible in time to first value.
Pilots start with agreed measures, a sponsor and a decision date, and revenue operations stages show pilot-to-contract conversion.
Quarterly reviews use traceable usage and customer-reported figures, giving renewal and investor talks firmer ground.
Health tech is crowded, and buyers and investors both look for proof that customers use the product and get something from it. AI adds discipline: knowing which accounts are real, answering reviews quickly, getting customers live and showing outcomes with sources. It does not replace a product that solves a problem buyers will pay to fix. Our GTM strategy work starts there.
General information for planning, not legal advice. Statuses and dates are as published at the time of writing.
We gather your past security, privacy and DTAC responses into one reviewed library with named owners, then use AI automation to draft from it with sources attached.
We define stages, success measures, a sponsor and a decision date for every pilot, and make the CRM show where pilots stall.
Before building, we run a function-level review: what the feature does, who sees the output, whether FDA or MHRA boundaries apply and what safety evidence buyers will ask for.
Keep a maintained library of approved answers with owners and review dates, and draft from it. A person must still confirm each answer is current before it goes to a buyer.
NHS-market digital health products need a DTAC assessment. It does not confer MHRA approval, and a product that is a medical device follows a separate route. DCB0129 and DCB0160 also apply.
No. McKinsey found health services and technology firms leading on implementation, which describes vendors adopting AI. It does not measure provider purchasing, so qualify demand with real pilots and committee access.
Only where a specific workflow and measure justify it, and after a function-level regulatory review. We help with discovery and evaluation design; a regulated device needs specialists and a quality system.
Yes. Trained executive assistants working with AI can draft updates from your CRM and finance data, and you review every figure. See AI executive assistance for how access and confidentiality are handled.
Book a discovery call and bring your last security questionnaire, a live pilot or an AI feature you are weighing, and we will agree where to start. Or send a first note through the contact page.