Clinician-supervised early intervention

More children within clinical reach.

ECI prepares structured observations and draft intervention plans from home video, so early-intervention clinicians can focus on the decisions only they can make.

Pre-revenue / Validation planned

Prototype interface / Synthetic case
Prototype clinician workspace: AI draft observations with Accept, Edit, Reject and Escalate controls. Zero of seven decided.
ECI Override Console prototype. AI draft observations, none yet decided. Synthetic demonstration. No patient data.
The clinician decides.
AcceptEditEscalate

No plan released before approval.

01/ The constraint

Reserve clinical time for clinical judgement.

The research question

Can ECI reduce clinician time per family without compromising review quality or appropriate escalation?

Early-intervention systems cannot add qualified clinicians on demand. ECI is designed to move preparation away from scarce licensed time, while leaving interpretation and accountability with the clinician.

  • ParentCaptures home evidence
  • AIPrepares structured observations and a draft plan
  • Licensed clinicianInterprets, corrects, approves or escalates
  • ParentCarries out approved activities
  • AI-supported follow-upPrepares later evidence for clinician review

02/ One case, end to end

From home evidence to an approved plan.

Prototype screens, sequenced for illustrationSynthetic demonstration. No patient data.

  1. 1Home evidence
  2. 2AI preparation
  3. 3Proposed plan
  4. 4Clinician review
  5. 5Correction
  6. 6Approval
  7. 7Family guidance
  8. 8Follow-up

Step 1 of 8 / Home evidence

Prototype clinician workspace showing the family video panel, timeline and a synthetic-demo label.View full size
ECI Override Console prototype, family video. Synthetic demonstration. No patient data.
Evidence received

A parent shares a short home video.

The family adds their concern and priority alongside the clip: “Single words often, longer phrases less consistent,” and “How do we support communication without stopping Spanish?”

The video stays the source of truth. Every later observation links back to a moment in it.

Family plan: lockedNothing reaches the family until a licensed clinician approves.

Advances the demonstration only. It never grants approval.

03/ First deployment

Start where the capacity problem is visible.

Age precision. IDEA Part C generally concerns infants and toddlers from birth through age two. ECI's developmental ambition may extend through age five. The first deployment focus is under three.
State IDEA Part C early-intervention systemsInitial buyer
Contracted early-intervention provider organizationsInitial buyer
  • 1Statutory timelines make delay visible.
  • 2Clinician supply cannot expand on demand.
  • 3Agencies need more throughput.
  • 4The problem is capacity, not lack of awareness.

04/ Next proof

The next proof is deliberately small.

The question is not whether AI can draft. The question is whether ECI can safely increase clinical throughput while keeping accountability with the clinician.

Planned validation
1clinician
10families
90days

Planned feasibility validation. No results claimed.

What will be measured

  • Total clinician review time
  • Clinician time per family
  • Correction burden
  • Override rate
  • Escalation rate
  • Appropriate insufficient-evidence handling
  • Throughput
  • Time to service
  • Family follow-through

The goal is not AI efficiency.It is more children reached early enough for intervention to matter.

05/ What compounds

Every clinician correction makes the supervision layer more valuable.

Structured clinician edits, overrides, escalation decisions and insufficient-evidence decisions can create proprietary supervision data tied to real developmental planning.

  • Correction + reason
  • Override + context
  • Escalation + outcome
  • Insufficient evidence + decision

Could become an asset for

  • Improving draft quality
  • Understanding where models fail
  • Measuring clinician trust
  • Identifying where human judgement remains essential
  • Improving evaluation and governance

Potential, not established. Defensibility depends on structured capture, appropriate data rights and demonstrated product improvement.

06/ Physician-trained. Parent. Builder.

Built from clinical training and lived experience.

Dr. Chioma Agha-Ayorinde

  • Physician-trained
  • Imperial College London MBA, Distinction
  • Oxford Executive Diploma in Artificial Intelligence for Business, candidate
  • Harvard Medical School Executive Education
  • Mandela Washington Fellow
“I understand both sides of this system: the clinical constraints, and the reality of trying to carry a child's plan into everyday life.”

Meet the founder.

Discuss the clinical workflow and the next validation.

Your details go to ECI only. They are not published or shared.