eduba Prepared by Eduba for Bea Better Eating Inc. — Emerge Americas 2026
Prepared for Bea Better Eating

A note from Eduba, left at your booth.

A read on how Bea's clinical voice scales when the team stays small.

Seventeen years of hypnosis and EMDR work. Two therapy practices in New York. A clinical judgment that decides which craving gets which intervention. Bea works because that judgment is threaded through every response.

30 minutes with Matt

Matt Creamer, CRO at Eduba. Bring one AI response you wish had gone differently.

The question

How the clinician stays in the machine as the machine gets busier.

The question that shows up as the user base grows, as the content library expands, and as an employer or a health plan eventually picks up the phone, is how that clinical judgment stays threaded when there are ten times as many interactions running in parallel.

The Eduba frame

Hypnosis. AI. Therapy. Each belongs on a different layer.

We work on computational orchestration. Sixty percent of most products are traditional code and database work. Thirty percent are rule-based logic. Ten percent are real AI problems.

60% Traditional code and database work
30% Rule-based logic and guardrails
10% Genuine AI work. For Bea, this is the clinical voice inside the chat and the hypnosis track selection.

The way to protect that ten percent is to codify the context around it. A layered architecture that makes the founder's judgment reviewable, testable, and editable by a second person without losing the thing that makes it defensible. That is the scope of the audit we would run.

A similar shape we have run

Feeld. A product company working through how to operationalize AI inside a team that could not afford to blow up the user experience.

Buyer

Feeld (CTO-led engagement)

Shape

Scoped sprint

Deliverables

Workshop, advisory calls, Organizational Context Architecture, Strategic Operations Framework

Time to usable artifact

The week after delivery

Bea is the clinician-led mirror image of Feeld. The same sprint shape fits. The headline ask is help us architect the context so the AI behaves like the founder would, repeatably, at scale.

The methodology is published

Interpretable Context Methodology (ICM).

Founder IP at L0. Clinical guardrails at L1. Dynamic user context at L3. Working artifacts at L4. A layered filesystem that gives the Bea team's clinical judgment a home the rest of the team can edit safely.

Submitted to ACM TiiS. Repo is open, MIT license. github.com/RinDig/Interpretable-Context-Methodology-ICM-

About Jake, briefly

Marine Corps veteran. MSc Future Governance, University of Edinburgh. Published in ACM TiiS and arXiv. 1,500+ people trained across enterprise engagements (Pacific Life, Colgate-Palmolive, KPMG UK, one of the Big Four) since May 2025. Jake also built an online community to 22,000 members in five weeks, which is relevant here only in that distribution and retention instincts translate.

The ask

30 minutes with Matt.

Bring one AI response you wish had gone differently. We will walk it backward through the context layers and show you where the architecture would have caught it.

Book 30 minutes with Matt Creamer

Matt Creamer, Chief Revenue Officer, Eduba. calendly.com/thecro-eduba/30min