BEHAVIORAL SIGNAL
Don't wait for traffic to learn from behavior.
Loop runs human-like synthetic users through your prototype, product, or live site, showing where they click, hesitate, drop off, and convert, even when real sessions are limited.
See what is not working before real traffic makes it expensive.
Storefront
New collectionSummer collection
Essential tote
A carry-all for every day.
- Test cycles from days to minutes
- Any traffic stage
- Replay-backed findings
- Any product surface
HOW THE SIGNAL IS MADE
From your screen to a decision.
Input
URL · prototype · screen
Run
100 users complete a task
Output
Funnel · friction · heatmap · replay
Decision
What to change to drive the target action
ONE FINDING, END TO END
Every finding carries its evidence.
This is an illustrative example of the output format, not a measured result.
- Finding
- Price-sensitive users hesitate at the fee disclosure.
- Segment
- 38% of budget-conscious users
- Evidence
- 19 session replays
- Behavior
- Repeated backtracking before event selection
- Recommended test
- Disclose estimated fees earlier
WHO THE SYNTHETIC USERS ARE
Not demographic costumes.
Behavioral users shaped by intent, confidence, urgency, and constraints.
A persona that is only a demographic label will behave like a stereotype. Loop shapes users along the dimensions that actually change what someone does on a screen.
- intent
- urgency
- price sensitivity
- trust sensitivity
- digital confidence
- prior knowledge
- search vs browse
BEHAVIOR
Behavior, not a report
Other synthetic-user tools tell you what users say. Loop shows what they do, on your real screens, with a heatmap, a funnel, and a replay behind every finding. No live traffic needed.
PROCESS
How it works
- 01
Drop in a URL, a web or mobile app, or a screen. No SDK or instrumentation.
- 02
Shape human-like behavior (persona, intent, difficulty), or use digital twins of your real users.
- 03
Get signal: heatmaps, drop-off, feature friction, and a replay of every session, each tied to on-screen evidence, so nothing is a guess.
OUTPUT
What you get back
Attention and click heatmaps per screen.
Task funnels with exact drop-off points.
Feature friction, ranked.
Conversion, message, and pricing reaction: what lands, by segment.
Session replays as proof behind every finding.
- Landed100%
- Viewed item82%
- Started checkout43%DROP-OFF · 82% → 43%
- Completed41%
THE DIFFERENCE
The next layer of UI testing is product use.
Interviews ask what someone might do. Attention models predict where someone might look. Analytics waits for real visitors. Loop adds synthetic task attempts on your product surface, with replay, funnel, heatmap, and friction evidence behind each finding.
Instead of
Synthetic interviews
Ask what someone might do
You get stated intent, not product behavior. The AI reacts to the idea, copy, or screen, but it does not actually attempt the task inside your flow.
Loop
Task attempts on your product surface
Synthetic users move through your URL, prototype, or screen with a target action, so the output is based on clicks, hesitation, drop-offs, and replays, not only stated intent.
Instead of
Attention / salience heatmaps
Predict where attention may go
You get a visual priority map, but not the task logic behind it. It can show where attention may concentrate, yet still miss why the target action fails or what blocks the next step.
Loop
Behavior tied to the task
Loop's heatmaps come from synthetic users attempting the task. Attention is connected to friction, paths, failed steps, and whether the intended action actually happens.
Instead of
Analytics & session tools
Observe real visitors after they arrive
Real sessions are valuable, but learning only on live traffic can be slow, costly, and risky. If users get frustrated or miss the target action, the business may already be paying through lost conversion or weak engagement.
Loop
Synthetic sessions before business risk
Run controlled behavior tests before relying on live traffic, or use existing behavior patterns to shape synthetic cohorts for new flows, messaging, pricing, and onboarding tests.
Why we don't give a single accuracy score
We do not compress behavior into one accuracy claim. Loop shows the evidence behind each finding: the task, the synthetic user group, the replay, the funnel, the heatmap, and the friction. A session is more useful than a percentage.
Why we won't quote you an accuracy numberWHY NOW
AI made everything faster. User testing stayed slow.
Building, design, copy, launch: all faster now. Testing with real people didn't change. Three things changed that make closing that gap possible.
LLMs are finally good enough
Imitating human behavior convincingly is recent. The method didn't work three years ago; it does now.
The vibecoding wave
Tools like Lovable and Rork created a new kind of builder: shipping fast, with no traffic, no panel, and no budget for a research agency.
The agentic web is coming
Soon some of your visitors won't be human. They'll be agents acting for people, and the same engine that watches humans will watch them.
WHY THIS WORKS
Loop is early. The method isn't guesswork.
We do not have our own parity numbers yet, and we will not borrow anyone else's. Here is the published research the approach stands on.
85%
as accurately as people replicate themselves
Generative agents reproduce real people
Researchers simulated 1,052 census-representative participants from two-hour interviews. The agents replicated those participants' survey responses 85% as accurately as the participants replicated themselves two weeks later.
Park et al., Generative Agent Simulations of 1,000 People925
human forecasters the silicon crowd matched
The silicon crowd rivals the human crowd
An ensemble of 12 language models was compared against 925 human forecasters across 31 questions. The LLM crowd was not statistically different from the human crowd.
Wisdom of the Silicon Crowd28%
of what people do is explained by what they say
Stated intent predicts behavior poorly
A review of ten meta-analyses covering 422 studies puts the correlation between stated intention and actual behavior at r ≈ 0.53. Intentions explain only about 28% of what people actually do. This is why Loop watches behavior instead of asking.
The say-do gapThese studies support the underlying methods and the need to observe behavior. They do not constitute validation of Loop's product-specific predictions.
And we didn't just cite the research. We tried to break our own method, and reported what broke.
Read the field reportTRUST BOUNDARIES
What Loop reads well, and what it doesn't.
What Loop reads well
Behavior on a screen: paths, drop-off, friction, and where attention goes.
What still needs humans
Motivation, nuance, novel context: anything where you need to ask why.
How findings are backed
Every finding links to a session replay and on-screen evidence. If we can't back it, we don't report it.
Honest answers
FAQ
Does it replace real user testing?
No, and it isn't meant to. Loop is a faster, cheaper behavioral layer underneath the research you already do: less recruiting, shorter cycles, more iterations. Save real people for the nuanced calls.
Is it just a UX tool?
No. It shows the whole reaction (conversion, messaging, demand), not just design friction.
How do I trust the findings?
Each one is tied to a session recording and on-screen evidence. If we can't back it, we don't report it.
Do I need traffic?
No. Loop works at any traffic stage: a prototype before launch, a new funnel, or a live site where real sessions are too thin or too slow to learn from.
How hard is it to set up?
Point us at a URL or connect your web or mobile app. No SDK rewrite, no event tagging. Setup takes minutes.
Can I use my real users?
Yes. Add digital twins of your active users, so tests reflect your real base instead of a generic persona.
What about agents and the agentic web?
The same engine extends to agent visitors. As agents start using your product, we'll show you what they need to find.
BUILT WITH THE FIRST TEAMS
Built with the first product teams
Loop is opening to a small group of teams testing products before launch. Early teams get direct access to the product team and help shape how behavioral testing should work. We're early, and we'd rather show you the sessions than a slide deck.