AI Recommendation Intelligence
When a customer asks ChatGPT, Claude, or Gemini to recommend a product, what makes the AI pick your brand? Reconnix measures the 26 signals that drive AI recommendations across 10 frontier models, so marketers can see — and fix — what's holding their brand back.
of signals work cross-model
Universal optimization is viable
correlation with human behavior
AI ≠ human psychology
model-specific swing
When models diverge, it matters
A/B preference comparisons
Third Party Authority
Genome cosine similarity range
What is APIS?
The AI Purchase Intelligence System (APIS) is a pre-registered research study measuring how frontier AI models respond to different content signals when making purchase recommendations. Using a forced-choice A/B methodology, we quantify the causal effect of 26 distinct content dimensions on AI preference.
This research provides empirical guidance for businesses optimizing content for AI-mediated commerce. As AI agents increasingly influence purchase decisions, understanding what makes content "machine likeable" becomes critical for digital success.
Machine Likeability Score Calculator
Score any product URL against all 26 dimensions and get optimization recommendations.
Model Genomes
Explore unique behavioral profiles for each AI model across all dimensions.
26 Dimensions
Deep dive into each psychological dimension and its effect on AI recommendations.
Cluster Breakdown
Six thematic clusters organizing 26 dimensions. Mean effect size across all clusters: h = 0.265