Reshaping MedTech with AI

How AI Agents are revolutionizing the way we operate and innovate.

Every day, our Waterston team comes to work in the high-stakes, high-complexity world of MedTech innovation. At the beginning of every meeting, we review our Waterston Mission, which states that we are stewards of inventions, creating the optimal conditions to help them grow from idea to execution—a task we accomplish by identifying the right technologies and bringing them to market in the fastest, most profitable way.

It’s the word “fastest” that had us first curious about and now welcoming the rise of AI agents. These autonomous software systems are capable of perceiving, reasoning, planning and taking action to achieve specific goals, allowing them to become an operational backbone of MedTech venture building. Their ability to conduct deep due diligence on incoming technologies and streamline entrepreneurial workflows is revolutionizing how MedTech start-ups are built, scaled, and exited. They go beyond mere tools, acting as our collaborators, co-pilots, and accelerators, enabling us to move more efficiently than we ever dreamed possible.

At Waterston, we are piloting AI agents for four use cases:

1. Intellectual Property Due Diligence

Due diligence on IP is one of the most critical—and resource-intensive—components of what we do at Waterston. If we make an early misstep in patentability, freedom to operate, or analysis of artwork, one of our ventures can be derailed for months or even years.

AI agents offer us an unprecedented ability to conduct rapid, structured, and repeatable IP due diligence by:

• Parsing patent databases (USPTO, EPO, WIPO) for relevant filings across jurisdictions

• Classifying prior art based on semantic similarities, not just keyword matching

• Analyzing claim language for novelty, obviousness, and enforceability

• Mapping competitive IP landscapes, which includes blocking patents, licensing risks, and finding open domains

A due diligence agent can take a one-page description of a novel catheter and return a synthesized report of similar filings, inventors, litigation history, and whitespace opportunities—all within hours. This dramatically shortens our “go/no-go” decision window and empowers our Phase I team to engage with TTOs and inventors using a well-informed, strategic approach to licensing.

2. Standardizing Entrepreneurial Workflows

Venture firms and incubators that spin out MedTech companies historically have relied on experienced entrepreneurs-in-residence (EIRs) to validate, develop, and de-risk early-stage opportunities. But, the processes they follow—market sizing, clinical problem framing, KOL engagement, regulatory mapping—are often completed manually, causing them to be siloed and inconsistent across projects.

At Waterston, we are leveraging AI agents to enable standardized, intelligent workflows by acting as embedded co-pilots within the company development process. These agents:

• Guide the EIR through our defined strategic playbook, prompting them to complete structured frameworks like the Waterston 9 Blocker

• Auto-generate market research summaries, pulling from payer databases, CMS pricing, 510(k) precedents, and investor databases

• Track tasks and milestones across opportunity funnels, ensuring each spinout hits key diligence gates before accessing the next tranche of financing

• Identify surface benchmarks, including typical reimbursement rates, FDA submission timelines, and unit economics of similar products.

Over time, the agent learns from past portfolio company data and EIR inputs to refine the process, offering tailored recommendations based on therapeutic area, modality, or stage of development.

We like to think of it as a “checklist with context”—not rigid or generic but dynamically updating based on company progress and ecosystem signals.

3. Searchable Company Databases with Generative Responses

Firms that manage portfolios of MedTech companies—or evaluate inbound deals—are often sitting on a wealth of unstructured or inconsistently tracked data—diligence notes, pitch decks, operating models, meeting minutes, regulatory pathways, to name a few. But when this data is locked in static folders or CRMs, its value is diminished.

AI agents powered by retrieval-augmented generation (RAG) have the ability to turn these archives into searchable, generative intelligence layers.

Imagine asking an internal agent:

• “Which of our portfolio companies have predicate devices under product code DQY?”

• “Can you summarize all Class I companies we evaluated last year and their reasons for rejection?”

• “What was the barrier we identified for our neurostimulation company regarding CMS reimbursement?”

Rather than manually searching through Google Drive, emails, or Slack messages, our team receives conversational responses grounded in firm-specific knowledge along with citations to source documents.

These agents act as institutional memory and accelerate company development and EIR productivity by making internal knowledge usable in seconds.

4. AI Co-Pilots for Sell-Side M&A in MedTech

We have found that, for MedTech startups, M&A is often the most likely exit—especially in the middle market where strategic buyers prioritize tuck-ins over IPOs. But preparing for acquisition is labor-intensive, error-prone, and time-sensitive. The success of a deal often hinges on how well a company tells its story, defends its valuation, and navigates diligence—all while continuing to operate the core business.

AI agents are emerging as powerful co-pilots for sell-side M&A, acting as virtual deal teammates. Some applications we are exploring include:

• Automating data room creation and curation by scanning company documents (e.g., IP, regulatory, quality, contracts) and organizing them according to acquirer expectations. This reduces back-and-forth and ensures completeness

• Anticipating buyer questions by simulating a diligence Q&A process, identifying weak spots in financials, compliance, or IP that need to be addressed proactively before buyer scrutiny

• Generating M&A narratives such as synergy models, strategic fit slides, and use-case scenarios that are tailored to each acquirer’s priorities. This enables a more personalized and compelling positioning in management meetings

AI agents provide leverage during a high-stakes, high-distraction period and free the team to focus on performance and strategic storytelling, while minimizing surprises during diligence.

For Waterston OpCos, which are aiming to create competitive tension, AI agents can support parallel buyer engagement by tailoring positioning materials to different buyer types (strategics vs. PE) and tracking conversations across multiple threads.

In short, AI agents shift the sell-side posture from reactive to proactive—arming our EIRs and advisors with speed, clarity, and confidence.

Why This Matters Now

The MedTech sector is at a unique inflection point with clinical innovation accelerating through robotics, digital surgery, and connected diagnostics on the one hand, and the process of building and scaling companies remaining highly manual on the other.

AI agents offer a lever to compress timelines, reduce error, and extract more insight from every dollar and data point. For venture operators like Waterston Capital, this is not just a technical upgrade—it’s a strategic advantage that positions us as a frontier firm. We believe AI agents are becoming the connective tissue that will tie diligence, development, decision making, and successful exits together. By mastering AI agents today, Waterston will be one of the front runners in shaping the MedTech landscape of tomorrow.