We help device and MedTech companies use AI in sales enablement, hospital evidence packets, clinical education and customer intake. Regulatory, quality and clinical staff keep control of anything tied to claims, complaints or the product itself.
AI helps the commercial side of a device business: assembling evidence packets for hospital value analysis committees, drafting tender responses from controlled content, routing leads and service requests, and producing training material. It does not belong in unsupervised complaint handling or quality decisions, and every claim must match the device's authorized intended use.
A device usually has to satisfy a clinical champion, a value analysis committee, supply chain, IT security and finance, each with different questions. A rep reaches only some of them, so the written material carries the argument.
FDA reported over 1,600 AI-enabled devices authorized for US marketing, which shows product activity, not buyer ROI. A 2025 review of NICE early value assessments for MedTech found frequent gaps in comparative effectiveness, implementation costs and equity.
AI-assisted sales claims still need to match the authorized intended use and be substantiated. A rep who pastes a draft into an email can move a statement past what the label supports without anyone noticing.
A customer email about a device behaving unexpectedly can be a complaint even if it reads like a service request. Complaint files are part of a regulated quality system, and the US Quality Management System Regulation, which incorporates ISO 13485, took effect on 2 February 2026.
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.
AI reads emails, web forms and call notes, classifies them, extracts product and lot details and routes them. Anything that could be a complaint or malfunction is flagged for a quality professional, who decides how it is handled. The classifier never closes a case.
AI assembles first drafts from a controlled library of approved answers, each linked to its source. Regulatory affairs approves any statement about performance or indications. The library needs maintaining as carefully as the product.
AI collects published evidence, internal data and implementation details into a draft packet answering the questions a committee typically asks about effectiveness and cost to implement. A market access lead checks every figure. The result is only as good as the evidence you hold.
AI scores inquiries from webinars, events and web forms against criteria you define, such as facility type and role, drafts the first reply and routes the lead to the territory manager. Humans own the criteria and the relationship.
AI drafts training outlines, quick-reference cards and webinar summaries from the approved instructions for use and published studies. A clinical education lead and a regulatory reviewer approve each version, and version control ties content to a product release.
A checker compares sales decks, emails and web copy with the authorized indications and flags language that goes further. We treat it as early because it can miss things, so it supplements regulatory review and never replaces it.
Approves every claim, indication statement and performance figure, and decides whether a new use of AI in sales material touches intended use.
Owns complaint handling and decides what counts as a complaint. Triage by AI feeds this role and never replaces it.
Checks that training reflects safe use of the current product version.
Owns the evidence and cost claims in every packet, working with AI governance rules on sources and review.
Own the customer relationship and are trained on what they may say or paste.
Reusable, sourced answers cut assembly time, measured as turnaround per tender and the share of answers reused unchanged.
Qualified leads reach the right territory manager sooner, shown in lead response time and inquiry-to-demo rate under clear revenue operations stages.
Quality teams receive structured reports with product and lot details, which should reduce back-and-forth with the customer.
One claims library and review routine reduce the chance of conflicting statements in decks, emails and web pages.
Device revenue builds across a long cycle: a clinician notices the product, a committee evaluates it, and service and training decide whether use grows. AI supports each handoff, from faster replies to better committee evidence and clearer answers after installation. GTM strategy sets which stakeholders get which message.
General information for planning, not legal advice. Statuses and dates are as published at the time of writing.
We follow a recent deal from first inquiry to decision, list each stakeholder's questions and find where the evidence packet failed to answer them. That becomes the template for AI-assisted drafts.
With your regulatory lead we build a controlled set of approved claims and sources, so every draft pulls from it and every change has an approver.
We classify incoming messages, flag possible complaints for the quality team and test the routing against past cases before it goes live, using AI automation with exception queues.
No. Claims come from your evidence and are approved by regulatory affairs. AI can help retrieve, format and check consistency against an approved library, but it should not originate efficacy statements.
For approved product information, yes. Anything that sounds like a complaint, malfunction or patient-specific advice must hand off to a person, because patient- or caregiver-facing recommendations can make software a device. See our AI agents work for escalation design.
No. We build internal tools, workflow products and integrations. Regulated device development needs a proper quality system and regulatory specialists, and we will tell you when a feature crosses that line.
Through workflow rather than warnings: drafting tools that draw only from the claims library, review rules and AI training on what may be pasted or sent.
Lead response time, inquiry-to-demo rate, evidence packet turnaround, the share of tender answers reused unchanged and complaint intake completeness, baselined before anything changes.
A discovery call can cover one recent hospital evaluation and your current claims process, so we can agree where AI helps and where regulatory and quality staff decide. If you would rather start in writing, the contact page is open.