Attention Insight
Predictive AI heatmaps before a design gets any traffic at all
Attention Insight generates predictive attention heatmaps using AI models trained on eye-tracking data, forecasting where people will look on a design without collecting a single real visitor. Designers upload a mockup, screenshot, or live URL and receive an attention map, a clarity score, and percentage of attention by region within seconds, which makes it usable at the design stage rather than after launch.
Overview
Every heatmap tool in this category has the same prerequisite: traffic. You cannot run a click map on a page nobody has visited, which means the most expensive moment to discover an attention problem, before launch, is exactly the moment behavioral tools cannot help. Attention Insight exists to fill that gap using predictive models validated against eye-tracking studies, reporting accuracy in the region of 90 percent against real eye-tracking results.
The practical output is threefold: an attention heatmap showing predicted first-glance focus, a percentage of attention by defined area of interest so a call to action can be quantified rather than described, and a clarity or focus score summarizing whether the composition directs attention or scatters it. For agencies and designers, that converts a subjective critique into a defensible number in a client presentation.
The honest framing is that this is a prediction of pre-attentive visual salience, not of behavior. It models where eyes go in the first few seconds, which correlates usefully with noticing but not with wanting, understanding, or buying. Used as a pre-launch check and a design argument it is genuinely valuable; used as a substitute for testing with real users it will mislead. Pricing is self-serve and modest, aimed at designers, agencies, and ad teams rather than analytics departments.
Best for
Designers, agencies, and ad teams who need to evaluate attention on a design, ad creative, or landing page before it goes live, and who want an objective figure to support a recommendation in a client review.
Not the right fit for
- Teams wanting to know what real users actually do, which requires behavioral analytics or testing.
- Anyone treating predicted attention as evidence of conversion impact, which it is not.
- Complex interactive flows, since the model evaluates static views rather than journeys.
- Organizations already running high-volume traffic where real heatmaps are available immediately.
- Research programs needing statistically defensible user data for high-stakes decisions.
How it works
- 1
You upload a design as an image, connect a live URL for a full-page capture, or work from a supported design tool integration. No tracking code and no traffic are involved at any point.
- 2
The model returns a predicted attention heatmap over the design within seconds, alongside a percentage of attention distribution across regions you define as areas of interest, such as the headline, hero image, or primary button.
- 3
Additional scores summarize the composition: a clarity or focus measure indicating whether attention concentrates on intended elements or scatters, plus a cognitive demand indication on higher tiers.
- 4
Designs are compared side by side, so two variants can be evaluated before either is built, and reports are exported or shared with clients and stakeholders as evidence for a recommendation.
Feature breakdown
20 features in 4 modulesPredictive analysis
The core model output, available with no traffic.- Attention heatmap
- Predicted first-glance visual attention rendered over any design, generated in seconds without users.
- Percentage of attention
- Quantified share of attention per defined area of interest, turning a design opinion into a number.
- Clarity score
- A composition-level measure of whether attention concentrates on intended elements or disperses across the layout.
- Focus map
- A view highlighting the regions likely to be seen first, useful for checking whether a call to action is in the path.
- Cognitive demand indication
- An estimate of how visually taxing a design is, flagging cluttered layouts before they reach users.
Inputs and workflow
Getting designs into the tool without friction.- Image upload
- Any static mockup, screenshot, or exported artboard analyzed directly.
- Live URL capture
- Point at a published page and the tool captures and analyzes it, including full-page scroll views.
- Design tool integrations
- Plugins for common design environments so analysis happens without leaving the file.
- Ad creative analysis
- Support for common ad formats and aspect ratios, a frequent use case for paid social teams.
- Batch analysis
- Multiple designs processed together for comparing a set of variants at once.
Comparison and reporting
Turning the output into an argument.- Side-by-side variant comparison
- Two or more designs analyzed together with attention distribution compared numerically.
- Exportable reports
- Shareable outputs suitable for client presentations and internal design reviews.
- Areas of interest
- Custom regions drawn on a design so specific elements can be measured rather than assessed by eye.
- White-label options
- Agency-oriented presentation on appropriate plans for client-facing deliverables.
- Project organization
- Designs grouped by client or campaign so a history of analyses accumulates rather than scattering.
Platform
Access, teams, and pricing surface.- Self-serve subscription
- Transparent plans metered by analyses per month rather than by traffic or seats alone.
- Team accounts
- Multiple users sharing a project workspace, aimed at design teams and agencies.
- Free trial
- Trial access with a limited number of analyses so the accuracy claim can be checked against your own work.
- API access
- Programmatic analysis on higher tiers for teams processing creative at volume.
- Validation documentation
- Published accuracy benchmarking against eye-tracking studies, which buyers should read before relying on the output.
Use cases
4 documentedAgency presenting a landing page redesign
The client prefers the existing hero image and the argument has become a matter of taste.
Attention distribution shows the current layout directing under a tenth of predicted attention to the call to action, and the redesign is approved on a number rather than an opinion.
Paid social buyer preparing a creative batch
Twelve ad variants are ready and the media budget will only support testing four properly.
Predictive analysis narrows the set by identifying creatives where the product and offer receive little attention, so live testing spend goes to plausible candidates.
Product designer working on an unlaunched page
The page does not exist yet, so no heatmap tool can help, and stakeholders want assurance before build.
A pre-launch attention check confirms the primary action sits in a high-attention region, and the design ships with one fewer avoidable mistake.
Ecommerce team auditing category pages
Dozens of templates need reviewing and there is no budget for research on each one.
Batch analysis flags the templates where promotional clutter dominates attention, and the worst offenders are prioritized for real testing.
Pricing
from From roughly $19 per month for a small monthly analysis allowanceSelf-serve subscription metered by number of analyses per month, with higher tiers adding team seats, API access, and white-label reporting. Annual billing is discounted.
| Plan | Price | Includes |
|---|---|---|
| Starter | From about $19 per month |
|
| Professional | From about $59 per month |
|
| Agency and API | Quoted monthly or annual |
|
Billing notes
- The meter is analyses, not traffic, so cost is driven by design throughput rather than site size.
- Batch and API usage consume allowance quickly for teams processing ad creative at scale; size the plan against creative volume.
- Annual billing carries a discount as published August 2026.
- There is no free plan, only a trial, so evaluation requires a short paid commitment or trial period.
- White-label output is tier-gated, which matters for agencies putting results in front of clients.
Value assessment: At entry pricing this costs less than an hour of design time and can prevent a hero layout that hides the call to action, which is a good trade for any team shipping designs regularly. The value collapses if the output is over-interpreted: it predicts looking, not buying, and no reasonable price makes that a substitute for testing. Treated as a pre-launch sanity check and a persuasion tool in client reviews, it is inexpensive and useful.
Strengths & limitations
Strengths
- Works with no traffic and no tracking code, which no behavioral tool can do.
- Results arrive in seconds, fitting inside a design review rather than after it.
- Quantified attention percentages turn subjective design debates into measurable comparisons.
- Inexpensive relative to any form of real user research.
- Published validation against eye-tracking studies rather than unsupported accuracy claims.
- Batch and API options suit teams evaluating large volumes of ad creative.
Limitations
- Predicts visual salience, not comprehension, motivation, or conversion.
- Static views only; interactive flows and scrolling behavior are not modelled as journeys.
- Accuracy claims are averages, and unusual layouts or imagery can fall outside the model's training distribution.
- Easily over-interpreted by stakeholders who hear heatmap and assume real users.
- No free plan for open-ended evaluation.
- Narrow scope means it complements rather than replaces any other tool in a stack.
Head-to-head comparisons
3 alternativesAttention Insight vs Hotjar
from Free (35 daily sessions on Observe Basic); paid Observe plans from roughly $32 per month billed annuallyOpposite sides of launch. Hotjar shows where real visitors clicked and scrolled, which requires traffic and time. Attention Insight predicts where eyes will land before anyone visits, which requires neither but is a model rather than a measurement. Use Attention Insight to avoid obvious mistakes pre-launch, then use Hotjar to find out what actually happened.
Full Attention Insight vs Hotjar comparisonAttention Insight vs Maze
from Free tier for limited studies; paid plans commonly from around $99 per monthBoth operate pre-launch, but with different evidence. Maze puts real participants in front of a prototype and measures task completion, which costs money and time but reflects behavior. Attention Insight returns a prediction in seconds for a few dollars. The sensible sequence is predictive screening to eliminate weak options, then a Maze study on the survivors.
Full Attention Insight vs Maze comparisonAttention Insight vs AdCreative.ai
from $39 per month (Starter)Adjacent tools for paid social teams. AdCreative.ai generates and scores ad creative aimed at production volume, while Attention Insight analyzes attention on whatever creative you already have, including designs it did not produce. Teams generating creative at scale often use both, one to make candidates and the other to sanity check them before spend.
Full Attention Insight vs AdCreative.ai comparisonImplementation & onboarding
- Setup time
- Minutes. There is nothing to install: upload a design or paste a URL and the analysis returns immediately.
- Learning curve
- Low to use, higher to interpret responsibly. The important discipline is stating clearly, especially to clients, that the output is a prediction of attention rather than a measurement of behavior.
- Onboarding
- Entirely self-serve with documentation and published validation studies. Agencies typically standardize a report format early so client deliverables stay consistent.
- Migration notes
- Nothing to migrate: analyses are outputs rather than accumulated data. Teams switching in or out simply keep exported reports as project documentation.
Platform, API & security
- Platforms
- Web applicationDesign tool pluginsAPI on higher tiers
- API
- REST API for programmatic analysis on higher tiers, used by teams processing ad creative or template libraries in volume.
- Compliance
- GDPR
- Data residency
- EU-based processing as a European company.
- SSO
- Not generally offered at self-serve tiers.
- Security notes
- The tool processes uploaded designs rather than end-user personal data, which keeps its privacy surface unusually small; unreleased creative is the sensitive asset and should be governed by normal confidentiality terms.
Support & resources
- Channels
- Email supportIn-app chatDocumentation and validation resources
- Documentation
- Concise product documentation supported by published accuracy studies and guidance on interpreting predictive output correctly.
- Community
- Presence concentrated in design and CRO communities, with content aimed at explaining what predictive attention modelling can and cannot tell you.
Company
- Founded
- 2019
- Headquarters
- Vilnius, Lithuania
- Ownership
- Private, independent
- Employees
- Small team (not disclosed)
- Funding
- Early-stage funding and grants; no large disclosed rounds.
Timeline
- 2019Founded in Lithuania to bring eye-tracking-trained attention prediction to designers without lab equipment.
- 2021Publishes validation studies benchmarking predictions against real eye-tracking results.
- 2023Adds areas of interest, comparison reporting, and design tool integrations for agency workflows.
- 2025API and batch analysis support teams evaluating large volumes of ad creative before spend.
- 2026Established as the pre-launch complement to traffic-dependent heatmap tools.
Integrations
- Figma
- Adobe XD
- Sketch
- Google Drive
- Zapier
Frequently asked questions
10 questionsWhat is Attention Insight?
Attention Insight uses AI models trained on eye-tracking data to predict where people will look on a design, producing an attention heatmap, percentage of attention per region, and a clarity score. It works on mockups, screenshots, ads, and live URLs, with no traffic and no tracking code required.
How accurate are predictive heatmaps?
The company publishes validation benchmarking its predictions against real eye-tracking studies, reporting accuracy in the region of 90 percent for first-glance attention on typical layouts. That figure is an average: unusual compositions, unfamiliar imagery, and highly interactive interfaces sit further from the training distribution and should be treated with more caution.
How is this different from a Hotjar heatmap?
A Hotjar heatmap measures what real visitors did, and needs traffic and time to produce. Attention Insight predicts what people are likely to look at, instantly, on a design that has never been published. One is measurement after the fact, the other is modelling before it, and they answer genuinely different questions.
How much does Attention Insight cost?
Plans start around $19 per month for a small monthly analysis allowance, rising with volume, team seats, API access, and white-label reporting. There is no free plan, but a trial is available so the accuracy can be checked against work you already understand.
Can predicted attention tell me whether a design will convert?
No, and this is the most important limitation to communicate to clients. The model predicts pre-attentive visual salience, meaning what draws the eye in the first seconds. Whether people understand the offer, trust the brand, or want the product are entirely separate questions that only real users can answer.
Is it useful for advertising creative?
Yes, and it is one of the more popular uses. Screening a batch of ad variants for whether the product, offer, or brand actually receives attention lets media budget go to plausible candidates rather than testing everything live, which is a meaningful saving when creative volume is high.
Do I need to install anything on my website?
No. You upload an image or provide a URL that the tool captures and analyzes. There is no tracking script, no consent implication, and no visitor data involved at any point, which is unusual in this category and simplifies its privacy position considerably.
Can it analyze a whole page including the part below the fold?
Yes, full-page captures are supported, though attention prediction is strongest for the initial viewport where first-glance behavior is best modelled. Below-fold analysis should be read as indicative rather than authoritative, since real scroll behavior varies enormously by intent and device.
Who should use this tool?
Designers, agencies, conversion specialists, and paid media teams who make decisions about layouts and creative before launch. It fits least well in organizations with high existing traffic, where real behavioral heatmaps are available immediately and a prediction adds little.
Does it replace user testing?
No, and buying it on that basis will produce bad decisions. It is a fast, cheap screening layer that catches obvious attention problems before they reach users. Anything consequential still warrants real testing, whether behavioral analytics on live traffic or a study with recruited participants.
Editorial verdict
Attention Insight solves a narrow problem well: knowing whether a design directs attention where you intended before anyone has seen it. That is exactly when it is cheapest to fix, and no traffic-dependent heatmap tool can help at that moment. The quantified attention percentages are also unusually effective in client conversations, converting a design argument into a comparison. The risk is entirely in interpretation, since a predicted heatmap looks identical to a measured one and stakeholders will not naturally distinguish them. Used as a pre-launch screen and stated honestly as a model, it is cheap insurance. Used as evidence of conversion impact, it is worse than nothing.
Written by the SaaSTracker editorial team. Awards, when shown, are judged against the published criteria in our methodology.