AI Innovation Labs · Class III life sciences

Reinvent R&D in Lifesciences

Accelerate faster to improve standard of care and transform patient outcomes

Why transform MedTech R&D

New FDA compliance paradigms are reshaping R&D. From SaMD to PMA, changes such as the PCCP framework and the current ISO 14971 are making Class III development more complex, with more moving parts and more evidence to keep up to date. To deliver faster, more affordable and more accessible devices, teams need a different operating model: live data, AI-assisted risk analysis, and a risk file that evolves with the product. That is how they advance pipelines, lift productivity and deliver better outcomes for patients.

Data
80%

of Class III risk failure is uncovered faster with AI than with traditional methods.

How MedTech R&D is changing

CONCEPT Risk hypothesis
FEASIBILITY Early FMEA
PIVOTAL · PMA IDE and submission
APPROVED Live risk file
Four stages, one risk record, kept live from concept through post-approval.

Dynamic risk management is advancing Class III R&D

Leading teams no longer treat the risk file as a document set frozen at design freeze. They feed it with live adverse event and complaint data, link every hazard to its software item under IEC 62304, and plan changes through a PCCP, so evidence stays current while the product keeps improving.

What we do

Triage competitor label claims and failure-mode RPNs side by side. Find the indications and performance claims no one owns yet, and which emerging designs can reach them first. Brighter cells mean a higher field-derived RPN; a dashed column is open territory.

Lead or seal failure
Software fault
Power loss
Sensor drift
Tissue response
Cybersecurity
Competitor A
Severe
Moderate
High
High
High
Moderate
Competitor B
High
Severe
Moderate
Severe
Low
Moderate
Competitor C
Moderate
High
Severe
High
Moderate
High
Claimed on labels
Claimed
Claimed
Claimed
White space
Claimed
White space
Open territory: Sensor drift and Cybersecurity. No competitor claims them on the label (illustrative).
Low Moderate High Severe White space · Illustrative; shows the format, not client data.
OutcomeAccelerate emerging designs and outmaneuver competitors on claims you can substantiate.

What you'll achieve

Faster pipeline decisions

Competitor risk profiles and live signals shorten the path from concept to a confident clinical plan.

Fewer late-stage surprises

Failure modes that matter surface early, before they become deficiency letters or design changes.

Audit-ready change control

PCCP-planned updates and a traceable risk file keep software and hardware changes defensible.

Claims you can substantiate

White-space maps point to claims and indications that are open and supportable with your evidence.

Our leader

Portrait of Thomas J Haenggi
Thomas J Haenggi
AI Practice Director
Life Sciences R&D Regulatory DC Underwriting Risk Analysis Agentic AI Risk & Value Engineering
[LinkedIn URL]

One engine, two markets

We built FMEA risk cards to underwrite data centers. The same discipline now governs Class III pipelines, with the FDA in the audit seat.

Data center underwriting
Class III life sciences
FMEA risk cards
FMEA risk priority numbers for competing devices and your own design, scored on one scale.
Live thermal and loss signals
Real-time adverse event reports, recalls and complaint trends from the device pipeline.
Policy wording analysis
Triage of competitor label claims and indications for use, to see what each device may say.
Governed AI and audit trail
Traceability from hazard to software item to submission, across IEC 62304 and ISO 14971.

A second operating unit

Everything above is AI enablement consulting. Everything below is a separate, turnkey AI product platform dedicated only to data center underwriting.

DC Underwriter Risk Intelligence Platform

DC Underwriter Risk Intelligence Platform

Transform insurance with AI-powered workflows for global data centers: liquid cooling, real-time pricing and FMEA risk cards in one governed view.

87%

of AI proofs of concept in data center underwriting never make it to production.

Figure from the current Phasefour site. Add the source before publishing.

  1. Executive alignment

    Align goals and workflows.

    Start
  2. Discovery sprint

    Identify friction and backlog.

    Sprint-based
  3. Proof of value

    Validate workflows and impact in real conditions.

    Production-ready MVP

AI readiness maturity framework: a structured, sprint-based path from executive alignment to a governed, production-ready MVP that scales.

Liquid cooling
Thermal health by rack row (illustrative)
Stable Watch Alert
Charts marked illustrative show the platform's format, not client data.
AI Innovation Labs (Consulting): insurance practice

AI-powered insurance

Automate insurance workflows to accelerate decisions, improve efficiency and reduce risk. Streamline claims, underwriting and servicing with AI-powered workflows built to scale.

Workflow automation

Streamline underwriting, claims and servicing workflows.

Cloud-native platform

Built on Azure, Fabric and Copilot technologies.

Governed AI

Manage compliance and risk controls.

Explore our AI Data Center Thermal Management Showcase and discover how we can supply change, together.

What's trending

Perspective · sample title The living risk file: planning device changes with a PCCP before you file Method · sample title Reading adverse event reports as live FMEA inputs Guide · sample title IEC 62304 for SaMD in a PMA: where software hazards meet the dossier

Bring us the Class III programme your risk file can't keep up with.

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Phasefour provides strategy and analytics services. Nothing on this site is a regulatory submission or regulatory advice. Charts marked illustrative show format, not client data.