UofTHacksBorn from UofT Hacks

Embedding Identity
to Components

We help create dynamic experiences based on user's behavioral identity. Adaptive UI that responds to who your users truly are.

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Business A - Shoes

E-commerce demo showcasing personalized shoe recommendations

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Beyond A/B Testing

Stop optimizing for the average user. Our AI creates a unique experience for every individual, adapting in real-time to context, intent, and behavior.

The "Segment of One"

Hyper-Personalization

Optimize for individuals, not averages. The engine maintains a profile for each user. If they prefer dark mode, high-density data, and technical jargon, the AI generates that specific UI every visit.

Maximize conversion probability for every single session dynamically.

Beyond On-Site Behavior

Context-Aware Adaptation

Expand inputs to include environmental context. UI changes based on time ("Late night snack?" vs "Lunch special"), location, and user preferences for ambience or places.

Dynamic SERP alignment rewrites H1s to match search intent perfectly.

Frustration Response

Rage Click Detection

Detects rage clicking or rapid scrolling as signs of frustration. Instead of just trying a new design, it triggers "simplification mode" with minimal UI, removed distractions, and a clear help button.

Turn frustrated users into converted customers.

Shared Intelligence

Cross-Site Federated Learning

Deploy across multiple websites to learn universal truths. If "Green Buttons" perform poorly across fashion sites this week, preemptively stop suggesting them for new clients.

Jump-start optimization for new clients in the same vertical.

Works Everywhere

Framework Agnostic

Integrate with any frontend: React, Vue, Angular, Swift, Flutter, or vanilla HTML. Our SDK adapts to your stack, not the other way around.

One platform, every framework.

Search Query Alignment

Real-Time Intent Matching

When a user lands via "durable hiking boots", the engine instantly rewrites headlines and descriptions to emphasize durability and hiking, matching intent perfectly.

Convert searchers by speaking their language.

Identity is Dynamic,
Adapt Experiences

Stop treating users as static segments. Embrace continuous identity signals.

Static Personalization

Rule-based systems

  • Manual segment creation
  • Stale user profiles
  • Limited behavioral signals
  • One-size-fits-all fallback
  • Complex integration
  • Slow iteration cycles

html.ai

Identity-first approach

  • Auto-learning identity models
  • Real-time behavioral adaptation
  • Multi-signal intelligence
  • Graceful degradation
  • Drop-in integration
  • Instant experimentation
Start Building

A/B Testing Platforms

Experiment-heavy approach

  • Requires statistical significance
  • Binary test outcomes
  • No individual personalization
  • High traffic requirements
  • Manual hypothesis creation
  • Delayed insights