For medical device manufacturers
Regulated work needs tools built for the work
Being compliant and being able to show an FDA investigator you're compliant are different jobs. Medical device companies run on documentation that has to do both: investigations, design history, risk files, complaint records. Much of it follows patterns an agent can prepare. Very little of it fits a generic tool.
The burden scales with the device. A Class I instrument maker and a Class III maker of life-sustaining electronics carry very different design-control, risk-management, and inspection loads. Mare & Oak knows how to scope automation, applications, and data foundations to your device class and regulatory exposure, on top of the systems you already run.
The first draft is the work to hand off
-
75–80%
productivity gains on first drafts of SOPs and deviation and CAPA reports in early pilots, leaving teams to concentrate on final review and approval.
McKinsey & Company, “Reimagining life science enterprises with agentic AI,” 2025. Early pilots and assessments across pharma and medtech operations.
-
15–20%
of medtech R&D capacity that agents could free up by taking on work like design controls and risk management.
McKinsey & Company, 2025. Modeled estimate for medtech R&D.
A CAPA record, a design history update, or a risk assessment can't be reviewed until someone has gathered the inputs and written a first version. Design controls and risk files are where that assembly gets heaviest, because every change has to trace back through requirements, verification, and hazards. That preparation follows patterns, which is exactly the work agents handle well. Hand it off, and your specialists spend their hours on review instead of assembly.
Agents that prepare the documentation
Agents gather the records, draft the first version of the investigation, design control update, risk assessment, or procedure change, and route it for review. Your quality team owns the root cause, the decision, and the sign-off.
Book a Call →Half the work needs a tool built for it
-
70–80%
of medtech workflows contain tasks that AI agents could take on or speed up.
McKinsey & Company, 2025. Modeled across 270 workflows.
-
50%
of life sciences workflows are complex and domain-specific: work McKinsey expects companies to support with custom-built agents.
McKinsey & Company, 2025.
The opportunity is broad, but most of it isn't generic. Complex, domain-specific work is where general-purpose software makes your team work around the tool instead of with it. We'd rather build the tool around the work.
Smaller manufacturers feel this most, because much of the evidence behind a CAPA or a compliance claim comes from outside the company: QA consultants, independent test labs, peer-reviewed literature. The right tool brings that outside evidence into the record, tracked and ready for review.
Applications shaped around regulated work
Purpose-built workspaces for work that generic tools handle poorly, such as a technical documentation workspace that keeps source evidence, outside reviews, linked documents, and review history in one place.
Book a Call →If it isn't documented, it didn't happen
You need to know that every agent works with access scoped to its job, and every action it takes is logged against the record it touched. Connect the systems underneath and that trail runs unbroken from complaint to investigation to CAPA to design change. Mare & Oak helps device manufacturers ship internal tools with confidence and the validation evidence to show an FDA investigator what it does and who approved its output.
See how we work →The foundation matters more than the next tool
-
84%
of life sciences CEOs agree that to optimize the impact of medtech AI, “we need to focus on data quality and integration rather than new tools.”
EY, Pulse of the MedTech Industry, 2025, citing the EY-Parthenon CEO Outlook Survey.
McKinsey's research on agents makes the same point from the other side: they need "a robust data infrastructure" and an organization aligned on "data ontologies and ownership." When systems describe the same event in different terms, every agent built on top of them inherits the confusion.
A data foundation agents can rely on
Quality, complaint, service, and commercial data connected under shared definitions, so trends show up across sources and agents work from the same facts your team does.
Book a Call →