Moderated sessions are slow
A week to recruit, a week to schedule, a day to run 5 sessions. Meanwhile the feature shipped and the next one is already in review.
AI usability testing that catches friction live.
Diaform is a usability testing platform that lets you collect feedback from users at the exact moment of friction: onboarding, first task, or checkout. AI-led sessions capture task-based usability feedback, IA and edge-case friction, and the "why" a static survey would miss.
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Why usability testing doesn't keep up with product velocity
Unmoderated tools give clicks, not reasons
Heatmaps and click-tracking show you where users struggled, but not why. You end up speculating about the reason, not acting on it.
Post-hoc surveys miss the specifics
A survey a week after a failed task gets you 'the app is confusing'. In the moment, the user could have told you exactly which button they expected to work.
Usability friction, caught in the moment
The AI probes a user right after they attempt a task, surfacing the exact step where the UI broke, not a generic 'it was confusing'.
Ridgeline Analytics
01
Probes for the why
Static forms accept vague answers. Diaform spots them and asks intelligent follow-up questions to uncover the root cause.
02
Dynamic & Adaptive
Real conversations can unfold in infinite, unpredictable directions — too many to map into a logic tree. Diaform doesn't build one; it just responds to each answer naturally, the way a person would.
03
Zero-Friction Launch
No login walls or scheduling links. Your audience starts instantly in their browser. No signup required. Works on any device.
How Diaform runs AI usability testing
01
Trigger at the UX moment
Drop the link into onboarding emails, failure states, cart abandonment flows, or post-task prompts. Capture the experience while users can still describe it.
02
Probing that finds the real issue
When a user writes 'it didn't work', the AI asks what they expected, what they tried, and what happened instead. Three short follow-ups often pinpoint the exact UI element that failed.
03
Voice for in-context responses
Users can describe what happened out loud, which tends to surface detail they'd never type. Especially useful for mobile and post-checkout flows.
04
Friction tags and severity
Every session gets tagged (navigation, copy, speed, state, error) with a severity signal. Across sessions, you get a ranked list of the patterns hurting the product.
05
Auto-summarized sessions
Each session returns a summary with the steps the user tried, where they got stuck, and the fix they implicitly asked for. Ship-ready for a design review.
06
Works on any device
Mobile, tablet, desktop, the conversation runs in-browser with no app install and no signup. A link is all you need.
How to set up AI usability testing
- 01
Pick the UX moment
Decide where to capture feedback, first-run onboarding, after a failed task, checkout abandonment, post-release prompts. The earlier in the struggle, the richer the response.
- 02
Brief the AI agent
Write the questions you want answered and upload the relevant product context. The AI uses them to stay grounded in your actual UI.
- 03
Drop the link in the flow
Embed in an email, in-app prompt, release note, or error state. Each user gets a private 1:1 session on their own device.
- 04
Review friction patterns
Per-session friction tags and summaries populate immediately. Across sessions, you get a ranked pattern view that makes the priority obvious.
Types of usability testing you can run
Cover the same usability testing techniques a traditional platform supports, without the scheduling tax.
Task-based usability testing
Give users a task, let them attempt it, then trigger the AI conversation right after. Capture what they expected, what worked, and what broke, task by task.
First-run and onboarding usability
Trigger after first login or first project setup. Get the early friction before it becomes churn, while the user still remembers the exact confusing step.
Checkout and conversion usability
Fire on cart abandonment or after a failed payment. The AI captures what stopped the user, pricing clarity, trust signals, form friction, or something you'd never have guessed.
Concept usability (mockup feedback)
Put a Figma link, a mockup image, or a prototype URL in front of users and let the AI walk them through it. Get usability reactions before you commit engineering.
Post-release usability
After a feature ship, prompt users who've touched the new flow. Catch the regressions and confusion your staging QA didn't surface.
Qualitative usability benchmarking
Run a consistent AI-led conversation before and after a redesign. Compare response summaries, sentiment, and transcripts to investigate how reported friction changed.
Traditional usability testing vs. AI usability testing
Traditional usability testing tools
- Recruit-and-schedule cycle takes days
- 5-10 sessions per study
- Clicks and heatmaps, but no why
- Post-hoc surveys miss the moment
- Synthesis is a manual week
Diaform AI usability testing
- Triggered in the moment, no scheduling
- Dozens of in-context sessions in parallel
- AI probing surfaces the why behind every struggle
- Conversation runs while friction is still fresh
- Tagged friction patterns ready for design review
Frequently asked questions.
The practical details behind setting up, running, and scaling this kind of research with Diaform.
AI usability testing uses an AI agent to ask participants what they tried, what they expected, and where they got stuck. Diaform returns a structured response and transcript for each session; it does not record the screen or automatically combine sessions into a usability report.
More conversations your team can run.
Explore the other ways Diaform can turn a static request for feedback into a useful conversation.
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