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FHIL · Health innovation verification studio

FHILabs

Before you commit capital, see whether the health model can work in the real system.

FHIL helps foundations, public agencies, health investors, and health systems test a proposed health model before funding, adopting, or expanding it.

Through ATLAS, our build-and-test engagement, we build a first working form, observe its use in a real setting, and turn the findings into a recommendation you can defend.

How ATLAS works
  1. BuildMake the first form
  2. MeasureObserve real use
  3. LearnRefine the model
Use the learning to build again
Decide

Proceed, add conditions, redesign, test further, or stop.

The test stays bounded. Your institution retains authority over current care, safeguards, and funding.

Two questions for the leader deciding what to back

Which part of care do you want taken apart and rebuilt for the age of AI—and what do you want to be known for having made possible for patients?

When to bring us a problem

  • People are getting stuck in a program you already run.
  • A model works elsewhere. You need to know whether it can work here.
  • You need to test a new approach before funding or expanding it.

Start with one task that matters to a patient or the person supporting them. Find what prevents it from getting done. Build and test the response before committing to a wider rollout.

Three engagements, by decision need

Choose the work your next decision needs.

Each engagement has an agreed scope, deliverables, and fee.

For ATLAS, we also agree access, permissions, milestones, and the checks the working form must meet. The delivery date reflects your decision and the work required.

Further implementation and ongoing support are scoped separately. You do not have to commit to them to commission the first test.

How FHIL works

From idea to evidence.
Then your decision.

We follow one healthcare task from start to finish. The barrier may be missing information, a broken handoff, or lack of access to care. We establish what needs to change before choosing the technology. We build or configure software, including AI tools, when the task calls for it. Existing tools may be enough.

  1. Build a small working form

    Make the idea tangible enough to test through one complete pathway.

  2. Test with the people it must work for

    Observe intended users and operators in the real setting, within agreed safeguards.

  3. Examine what held up

    Record what worked, what failed, and what remains uncertain.

  4. Hand over the work and recommendation

    You receive the working form, a source-traceable decision record, and a short work plan.

Your institution retains the decision.

FHIL provides the evidence and recommendation. You decide what happens next.

  • Proceed
  • Add conditions
  • Redesign
  • Test further
  • Stop
Explore the ATLAS build-and-test engagement

One complete pathway can justify the next stage. It cannot, by itself, justify scale.

The work connects three realities:

01 · Institution and capital
Who would fund the next stage, operate the tool, and cover its ongoing costs.
02 · First person
Whether the intended user can complete the task within the defined safeguards.
03 · Real pathway
How the approach compares with current practice: handoffs, corrections, and the human work required.

Evidence in the work

Two commissions.
Different questions.

People holding hands in a community circle

Agora Community Mental Health

The question
A Swedish international foundation asked what faith-based mental-health support exists in Nigeria and where training, safeguarding, and referral gaps remain.
The work
Research findings, systems maps, learning materials, and a post-engagement report.
The decision value
A documented view of community roles and referral gaps to inform further planning.

Evidence limit: Commissioned research and systems mapping—not a live platform, completed clinical pilot, or improved care outcomes.

Read the Agora case
An older adult using a tablet at home

ATLAS Rare Disease AI On-Ramp

The question
A U.S. family foundation asked whether to fund a larger AI-assisted platform for a rare-disease patient community.
The work
A source-oriented knowledge base, patient on-ramp, prompt system, use boundaries, quality checks, and distribution package.
The decision value
The technical core was feasible. Distribution and paying demand were not established. The buyer funded two stages and paused further work based on the findings.

Evidence limit: The work did not establish sustained use, clinical effectiveness, scalable distribution, sustainable demand, or population outcomes.

Read the ATLAS case

Accountability

Victor Ladele leads every FHIL engagement.

Victor personally directs each engagement. He begins with the initial decision. He continues through the final operating form and recommendation.

His 22-year record spans clinical care, public-health institutions, WHO crisis response, UNICEF innovation, and institutional implementation.

Specialists join only when the decision requires their expertise. The client approves their participation.

See Victor's record and accountability model

An invitation to the health leader deciding what deserves support

The Call.

FHIL works with health leaders who see an ambition their institution cannot yet put into operation, and who will not ask that institution to carry risk it cannot defend.

You should not have to choose between the bold move and the defensible one.

Bring the ambition.

Email us to request a 20-minute conversation. We’ll review your enquiry for fit. If a conversation would help, we’ll reply to arrange a time.

Send three lines:

  • What you want to make possible.
  • Who it should serve.
  • What your board must be able to defend.
No form required. Do not include patient-identifiable or institution-confidential information.Request a 20-minute call Opens a draft in your email app. Nothing is sent until you send it.