TRUSTED AI FOR HEALTHCARE AND GRID & POWER
AI you can defend to an FDA inspector, a NERC auditor and your own operators.
For medical device, pharma and biotech companies, and for utilities and grid operators: governance, guardrails and validation built into AI from day one. Led by a founder with nearly 25 years at GE across healthcare and grid, most recently as global CSV/CSA process owner at GE HealthCare.
CERTIFIED
Held by our founder, who leads every engagement.
FRAMEWORK
Follows the NIST AI Risk Management Framework
Our method maps to its four functions: Govern, Map, Measure and Manage.
VALIDATION DISCIPLINE
Proven in both domains
GAMP 5 and FDA CSA in healthcare. Factory and site acceptance testing and commissioning on the grid.
Everyone is talking about AI. Few can tell you whether it is safe to use inside a validated system or a control room.
That is the question we answer. We bring the same discipline that makes medical software inspection-ready and grid systems reliable, and apply it to AI.
WHY A PERSON MUST STAY IN THE LOOP
Medical and grid software are not like other software.
In most businesses, a wrong AI answer costs time. In these two, it can reach a patient or switch off power to a city. That is why AI can recommend, but a qualified person decides, and every decision is recorded.
In healthcare and life sciences
A missed defect, a corrupted record or a batch released in error can harm a patient and trigger an FDA finding. A qualified reviewer signs off every GxP decision AI supports.
On the grid
A wrong switching action can trip a feeder, damage equipment or put field crews at risk, and the effect is physical and immediate. AI advises; an operator approves every action.
WHAT WE DO
Govern it. Validate it. Guard it.
01
AI governance
Clear rules before AI goes live: an inventory of your AI use cases, each ranked by risk, with policies, roles and approvals that follow the NIST AI RMF and support ISO/IEC 42001 readiness.
We also build governed AI agents
When you are ready, we build AI agents and automations inside these guardrails. Each one has clear limits on what it can do, and a person approves its important actions.
02
AI validation and assurance
Risk-based validation of the AI you use: generative AI assistants such as Microsoft Copilot, ChatGPT and Claude, AI features inside your vendors' software, and your own models. Intended use, testing, acceptance criteria, and change control when a model or prompt changes.
03
Guardrails and AI operations
Human review where decisions matter, approved data sources only, monitoring for errors and drift, and an incident process, so AI stays safe and secure after go-live.
TRUSTED AI IN YOUR INDUSTRY
The same discipline, applied to two very different risks.
HOW IT WORKS
How a Trusted AI engagement runs
01
Assess
NIST AI RMF: Map
We list where AI is used or planned, and rank each use by its risk to safety, quality and reliability.
We don't promise that AI is risk-free. We promise a controlled, documented and auditable approach.
02
Govern
NIST AI RMF: Govern
We set the rules: data access, security, validation needs and who approves what.
03
Validate and deploy
NIST AI RMF: Measure
We test the solution against its intended use, starting with a pilot, and document the evidence.
04
Monitor and improve
NIST AI RMF: MANAGE
We track results, errors and drift, and keep the controls current as models and rules change.
START HERE
Trusted AI Assessment
Start with a 30-minute call. Then a short, fixed-scope review: you receive your AI use cases ranked by risk, the gaps against the NIST AI RMF and your industry’s rules, and a guardrail plan your team can act on.