"The model is accurate, but users don't believe it."
Services / AI Product UX
AI has a trust problem.
We design for it.
Your model works. But black-box outputs scare non-technical users away. An answer nobody trusts is an answer nobody uses - and trust is a design problem before it's a model problem.
most teams
The trap
Most agencies treat AI products like regular software. They're not. Users need to know why the AI decided, how sure it is, and what happens when it's wrong. Miss those and adoption stalls no matter how good the model is. We design the trust layer - the part that turns an accurate model into a product people actually rely on - and can build it too.
The design problem
Users don't trust what they can't predict.
AI products fail on hesitation, not features - unclear inputs, invisible reasoning, outputs users can't verify. We design for trust: what the system is doing, why, and what happens if it's wrong.
we've heard this before
Sound familiar
"People override the AI even when it's right."
"When the AI is wrong, the whole interface falls apart."
"Our agency designed it like regular software. It isn't."
Who hires us for this
The brief changes by role.
The need for clarity does not.
We work with the person accountable for the decision and bring users and implementation teams into the right moments.
AI founder
Users try it once, don't understand the output, and don't come back.
Get an interface that explains itself, so trust builds instead of eroding.
Head of Product
The model works, but users can't tell when to trust it and when to double-check.
Get UX that makes confidence, uncertainty and errors visible.
Engineering leader
Devs are inventing loading states, fallbacks and error messages on the fly.
Get the AI-specific UX patterns - latency, uncertainty, failure - decided upfront.
What you get
A complete path from uncertainty to implementation.
The depth adapts to your product stage and the decision the work must support.
Explainability patterns
Show why the AI decided, not just what it decided.
Confidence indicators
Visual signals for how sure a prediction is - act accordingly.
Error-state design
The AI will be wrong sometimes. The interface shouldn't fall apart.
Human-in-the-loop flows
Clean handoffs between automation and human review.
Progressive disclosure
The right complexity for the right user at the right moment.
AI data visualization
ML outputs made scannable. People act on what they can read.
A fit if
- The model works but adoption lags
- Users are non-technical and skeptical
- Outputs drive real decisions with real stakes
- You measure trust, not just accuracy
Not the right time if
- The AI is a demo, not a product yet
- Users are all ML engineers who read logs
trial first
Process
Step 1
Trust audit
Where users hesitate, ignore or override the AI. Mapped from data.
Step 2
Pattern design
Explainability, confidence and error patterns for your model's behavior.
Step 3
Prototype & test
The trust layer tested with skeptical users before code.
Step 4
Ship & measure
Adoption and override rates tracked after launch.
3-day trial
Free trial
One real screen, designed. See how we work before any commitment.
The trial
Test us with a free 3-day trial
Every designer is on our team - no freelancers. Give us one real task and see.
01
Pick the task
Pick a small part of your existing product.
02
We solve it
We solve one real design challenge on it.
03
Watch us work
See our process, speed and communication.
04
Choose what next
Continue with a pod - or keep the work, no hard feelings.
Trust
Design partner
3months
Every designer trained on SaaS UX before client work
1:1
One dedicated designer, embedded like your own hire
Lead
Head of Design reviews everything that ships
40+
SaaS & AI products designed and built
Proof
Where this already worked.
78% reduction in support response time
How Clair AI Cut Support Response Time by 78% With Conversational Agent UX
Desisle designed Clair AI's white-label conversational agent platform in 14 days, cutting support response ti…
15-day concept-to-prototype sprint
15-Day Design Sprint: Building an AI Analytics Dashboard From Concept to Prototype
Desisle designed a complete AI-powered analytics platform for Adapt Insights in 15 days across 12 modules, fr…
Quick answers
Before you ask.
What does the 3-day trial include?
We design one AI interaction - an explainability or confidence pattern - so you see the approach. No card.
Have you designed AI products before?
Yes. Clair AI's support agent, BluePen's screenless product, and a 15-day AI dashboard sprint, among others.
Does this apply to copilots and agents?
Especially those. Anything where a prediction drives a user decision needs a trust layer.
Can you build it too?
Yes. Streaming, confidence and fallback subtleties survive better when the same team designs and builds.
What if our model is still changing?
Good - the trust layer should be designed alongside it, including how it behaves when uncertain.