AI Product Design · Seed → Series B
Your AI feature works. Getting users to trust it is the hard part.
AI features fail differently than normal software — silently, confidently, and in ways users can't always spot. I design the trust calibration, failure states, and human-in-the-loop controls that make AI features something people actually rely on.
Problems I solve.
What usually goes wrong with AI features
- Trust — no signal for confidence or sourcing, so users either over-trust the output or dismiss the feature entirely
- Latency — no feedback while the model "thinks," so the product feels broken or frozen
- Failure — hallucinations and wrong answers have nowhere to go; one bad output erodes trust in every output after it
- Control — nothing lets people edit, override, or correct the AI's work
- Adoption — without a clear path back to human control, people quietly stop using the feature
How I approach it.
Map the failure modes
Catalog what happens when the model is wrong, slow, uncertain, or silent — before designing the happy path.
Design trust calibration
Confidence indicators, sourcing, and explainability, so people know when to rely on the output and when to check it.
Build the human-in-the-loop
Edit, correct, override — every AI feature needs a clear path back to human control.
Validate with real usage
Ship narrow, watch edit and correction behavior, and use that signal to improve the model and the interface together.
Outcomes, not opinions.
Founding Designer
AI-generated Joint Value Propositions rated equal to or better than manual ones by 8 of 9 users, with 88% feature adoption.
Design Lead
Legacy internal tool with significant UX debt and no AI readiness. Led the redesign used daily by designers, developers, and operators across titles like Candy Crush.
If you're looking for an expert who understands your needs and can answer them simply — contact Jorge.Rémy Voet · Head of Design, DIGITALinkers
Questions.
Do you build the AI model, or just design the interface around it?
I design the interaction layer — prompting UX, output presentation, trust and error states. I'm not an ML engineer; I work alongside your ML team or API provider, not instead of them.
What if we haven't decided which AI features to build yet?
I can run a short discovery pass to find where AI actually adds value — and where it doesn't — before any design work starts.
How do you design around hallucinations and wrong answers?
By assuming they'll happen. Every AI feature gets a plan for low-confidence output: sourcing, confidence signals, and an easy path to correct or override the result.
Does this replace our prompt engineer or ML team?
No — it complements them. I focus on how the AI's output reaches the user; prompt and model work stay with your technical team.
What does "human-in-the-loop" actually look like in the UI?
Edit affordances on AI output, confidence indicators, an obvious way to reject or regenerate, and a clear record of what was AI-generated vs. human-approved.
Can you work with an AI feature we've already shipped?
Yes — I audit what's live, find where users are confused or dropping off, and redesign around actual usage and edit patterns.