Attention Predictor™ Powered by

AI vision for software

API-ready human attention prediction for modern software.

Turn any image into predicted heatmaps, focus order, reports on images and image areas, and attention scores delivered through API or MCP.

Talk to us API / MCP
  • 1.7M+sessions
  • 34M+fixations
  • 6.1%MAE
input model output
source creative
Pepsi Halloween creative used as the source image for attention prediction.
predicted heatmap
Predicted attention heatmap for the Pepsi Halloween creative.
Current focus Can silhouette 0.86 predicted attention score
01 Can silhouette 0.86
02 Headline 0.61
03 Pepsi mark 0.47

Why this is powerful

Trained on real visual behavior at scale.

Built on 1.7M+ eye-tracking sessions, 12K+ unique items, and 34M+ fixations, the model returns usable attention signals in seconds instead of after a full study cycle.

Eye-tracking
sessions 1.7M+
Unique
items 12K+
Fixations 34M+
Validation
accuracy 6.1% MAE
01
RealEye studies

Measured gaze, fixations, and viewing dynamics from real participants.

02
Model training

Patterns learned from validated human attention, not synthetic scoring heuristics.

03
Deployable inference

Teams call the technology through API or MCP and receive interpretable outputs.

Why it is legitimate

Based on RealEye eye-tracking, not synthetic guesswork.

RealEye measured the human behavior first. The predictor turns those patterns into a deployable layer for fast pre-screening, iteration, and decision support.

VAI metrics

The most important eye-tracking metrics, turned into clean attention data.

VAI (Visual Attention Insights) packages the three signals teams read first: how fast something gets noticed, how many people notice it at all, and how long attention stays once it lands.

How fast

VAI Speed

Areas most likely to capture initial attention quickly.

Initial notice
How many

VAI Reach

Areas most likely to be seen by the broadest share of viewers.

Viewer coverage
How long

VAI Hold

Areas most likely to sustain attention once they are seen.

Attention duration

Where it fits

For teams that need attention data before they need deeper validation.

Use predicted attention data to pre-screen creatives, support product decisions, and prioritize what deserves real human validation.

Market Research

Pre-screen creatives before committing research budget or media spend.

Human Research

Prioritize which hypotheses deserve full validation with real participants.

AI Agents Vision Support

Give agents a model of what a human is likely to notice first.

Design and Creative Systems

Quantify hierarchy and likely focus order across product and UX surfaces.

Implementation

One model, clean integration surfaces.

Use it as a service layer for applications, pipelines, or agents. The core contract stays simple: image in, attention signal out, then route the outputs into heatmaps, reports on images, or image-area reviews.

  • HTTP API for product teams and internal services
  • MCP integration path for AI agents and automation workflows
  • Outputs framed as heatmaps, ranked regions, reports on images, and image-area analysis
  • Useful for dashboards, QA loops, review surfaces, and batch analysis
See documentation
integration shape
curl https://app.attentionpredictor.com/api/projects/PROJECT_ID/contents \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "display_name": "Shelf hero creative",
    "image_url": "https://example.com/creative.png"
  }'
example output
{
  "success": true,
  "workspace": {
    "id": "...",
    "name": "RealEye",
    "slug": "realeye"
  },
  "project": {
    "id": "...",
    "status": "draft"
  },
  "content": {
    "id": "...",
    "display_name": "Shelf hero creative",
    "metrics_url": ".../metrics",
    "heatmaps_url": ".../heatmaps",
    "matrices_url": ".../matrices",
    "aois_url": ".../aois"
  }
}

Talk to us

Want to give your software vision?

For API access, MCP workflows, or a custom integration around predicted human attention, write to us directly.

contact@realeye.io