AI Product · 0→1 · Co-Founder & CEO

Making user research fast, affordable, and emotionally intelligent with AI.

Odaptos is an AI-powered UX research platform that reads how users feel — not just what they click — so any team can run rich research in hours, not weeks.

Year
2018 – Present
Incorporated 2020
Role
Co-Founder & CEO
Owned the entire design function
Recognition
CES 2023
Innovation Award
[ HERO IMAGE ] — Odaptos emotion-analysis / meta-results screen · drop a product shot here
The Problem

User research is slow, expensive, and out of reach for most teams — and the emotional “why” behind behavior stays invisible.

Insights take weeks, specialists are costly, and frustration, confusion, and delight never surface in traditional click-and-survey testing. A success metric can hide a frustrated user.

The Solution

Research that captures emotion, automatically.

A platform that lets any team run moderated and unmoderated studies, then uses AI to surface the emotional and behavioral signal automatically — turning the invisible “why” into visible data.

01
Emotion analysis
AI reads facial and vocal cues during sessions to map where users feel friction or delight — turning the invisible “why” into visible data.
02
Automated transcription & tagging
Sessions are transcribed and a collaborative tagging system lets teams cluster insights without manual grunt work.
03
User recruitment
Built-in recruitment so teams can source the right participants without leaving the tool.
04
Persona design
Research outputs feed directly into living personas, keeping the team aligned on who they’re building for.
05
Meta-results
A synthesis layer that rolls individual sessions up into patterns a whole team can act on.
[ IMAGE ] — Feature screenshots (5): emotion timeline, tagging, recruitment, personas, meta-results
Background

The idea started in 2018, during my Master’s at Paris I Panthéon-Sorbonne. I kept seeing the same gap: teams knew research mattered, but cost and turnaround put real, continuous user understanding out of reach — so most shipped on opinion.

The part that bothered me most was that even when teams did test, the emotional truth of the experience slipped through. A success metric can hide a frustrated user. I incorporated Odaptos in 2020 and set out to make emotional, evidence-based research something a small team could run continuously.

+ To add · optional
A real industry figure on the cost/time of traditional UX research, or % of teams that skip it — with a cited source. Only if real.
Research

Existing tools captured behavior — but not real-time emotion.

The key gap: tools captured behavior and self-reported feedback, but not real-time emotion — and they were priced and paced for enterprise research teams, not lean product teams.

+ To add · medium
Early discovery & competitive analysis — how many teams you talked to, tools benchmarked (UserTesting, Maze, Lookback), and why they fell short. Add a competitive matrix image.
+ To confirm
Primary user — e.g. product designers, PMs, and founders at small-to-mid teams who need insight fast and can’t staff a dedicated research org.
How Might We…

…let any team capture the emotional truth of a session — and act on it — without specialist researchers, big budgets, or week-long turnarounds?

Ideation

AI was the unlock on three fronts.

Speed
Automated transcription, tagging, and synthesis collapse days of manual analysis into minutes.
Emotion
Models reading facial and vocal signal expose the “why” that surveys miss.
Access
Automation drops the cost and skill floor, so a non-researcher gets researcher-grade output.
+ To add · low
Information architecture / core flows diagram — study setup → session capture → AI analysis → meta-results.
Design

As co-founder, I owned the entire design function.

Interaction design, UI, the design system, and the brand — all mine to set.

[ IMAGE ] — Design system / component library / emotion-timeline UI
+ To add · medium — where founding-designer credibility lives
2–3 sentences on the design system and key UI decisions: how you made dense research data legible, how the emotion timeline was visualized, the component system that let the product scale. Plus logo / color / type rationale.
Testing & Iterations
+ To add · medium
2–4 concrete iterations where user feedback changed the product (what users struggled with → what you changed → the result). Before/after screens are powerful. If iteration was continuous/in-market, say so honestly — “we shipped weekly and iterated on live usage” is a strong founding-designer signal.
Results

From a Master’s-project idea to an award-winning company.

CES ’23
Innovation Award
0→1
Idea to market
US · LATAM
Markets launched

Took Odaptos from a Master’s-project idea (2018) to an incorporated, award-winning company (CES 2023 Innovation Award), through Techstars Washington DC, and into the US and LATAM markets.

+ To add · highest impact
Your strongest real numbers — teams/companies using it, studies run, time-to-insight reduction, funding raised. One or two real metrics dramatically strengthen the whole portfolio. Add a customer quote if you have one.
Final Thoughts
What I learned
The hard design problem isn’t the model — it’s making its output trustworthy and legible to a human who has to make a decision in thirty seconds. Design was the difference between “interesting AI” and “useful tool.”
Next steps
+ Where Odaptos went next — or what you’d carry forward.
Most proud of
Taking emotion — the softest, most-ignored signal in product work — and turning it into something a team could act on, then getting the world (CES) to recognize it.
Next project →
AI Fintech [NDA]
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Like what you see?

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