Deploy synthetic behavioral agents to navigate your product before real users arrive — see where they hesitate, where they drop off, and what it would take to convert them.
Example simulation
Intent score
84
Friction score
31
Journey stage
Checkout
Top friction
Trust signal
Illustrative example, not live data.
The synthetic buyer architecture
Traditional analytics observe real humans after the fact. Intent creates behavioral agents that navigate your product first — each one carrying its own motivations, patience, and trust requirements.
1
Every agent carries a profile — patience, urgency, trust threshold, tech comfort, price sensitivity — modeled from real interaction patterns, not a fixed script.
2
Agents process your interface with simulated attention and decision fatigue — the same constraints real visitors bring to your product.
3
Agents respond to trust signals, urgency cues, and competitive context the way real buyers do, not a fixed click-path.
01. Before it reaches real users
Run synthetic agents across your experience before it reaches real visitors — find what breaks before it costs you a conversion.
02. Behavioral forensics
Map hesitation, trust decay, and cognitive overload at the moment they happen, not after a visitor has already left.
03. Competitive awareness
Understand how competitors' experiences are evolving and what it means for how yours needs to change.
System architecture
1
Give Intent a URL and define the audience you care about.
2
Intent maps your interface's structure and content before any agent runs.
3
Synthetic agents navigate the experience with realistic patience, attention, and trust thresholds.
4
Get a friction report, an intent score, and specific recommendations tied to where agents hesitated or dropped off.
Cart abandonment and checkout friction.
Trial-to-paid conversion and onboarding friction.
Trust signal placement in sensitive flows.
Supply and demand-side conversion journeys.
Procurement and lead-gen form conversion.
Signup and renewal flow friction.
| Capability | Basic analytics | A/B testing tools | Intent |
|---|---|---|---|
| Tells you what happened | Yes | Yes | Yes |
| Tells you why users dropped off | — | — | Yes, behavioral mapping |
| Requires live traffic to test | Yes | Yes (burns traffic) | No — zero-traffic prediction |
| Competitor structure scanning | — | — | Continuous monitoring |
See it in your dashboard
Every simulation produces a friction report, an intent score, and a recommendation set — the same information you'd get from watching a real user, without waiting for one.
Intent score
84
Friction score
31
Recommendations
6
Journey stage flagged
Checkout
Illustrative example, not live data.
Product walkthrough
1
Paste a staging link or production URL into Intent.
2
Select the traits and context that matter for this experience.
3
Synthetic agents navigate the experience with realistic behavior.
4
Get prioritized friction points and concrete recommendations.
Technical validation
How can a simulated user predict real human behavior?
Agents aren't scripted click-paths — each one carries a behavioral profile (patience, trust threshold, urgency, and more) modeled from real interaction patterns. Running many agents with varied profiles surfaces where an experience is likely to create friction, the same way usability testing with a small panel of real users does, but before you've spent traffic finding out.
What about the complexity of real-world decision-making?
No simulation captures every nuance of a real decision. Intent is built to surface the friction that's structurally likely to affect most visitors — unclear pricing, weak trust signals, confusing forms — not to predict any single individual's choice.
Can synthetic agents understand brand and context?
Agents are given the same context a real visitor would have: the page content, the flow they're in, and a defined audience profile. They respond to what's actually on the page, not a generic script.