AI doesn't need more intelligence, it needs context
The Personalization Layer for AI
Crate builds durable personal understanding that lets AI predict intent, make better decisions, and compound with use.
Today's AI is powerful, but still one size fits all.
Algorithms Guess
Based on clicks and views, missing the nuance of true preference.
Feeds Overwhelm
Endless content with no sense of what actually matters to you.
Prompts Assume
You need to know what to ask before you can find it.
The Crate Solution
Personalization isn't a feature you add later. It's the foundation everything else depends on.
We believe the next generation of AI won't be driven by bigger models, but by better user understanding. If AI can't model taste, it can't predict. If it can't predict, it can't act. This is the layer we're building.
Predictive
Adaptive
Signal-Rich
How Crate Works
A system that remembers what matters.
Crate builds a living model of your interests from the content you engage with, so AI can anticipate what matters next.
Proactive, Not Reactive
Agentic AI Feed
Our AI feed doesn't just recommend, it acts. It proactively curates collections, suggests products, and builds itineraries. It deepens engagement by connecting interests across categories like travel, food, and beauty.
Your Personalization Profile, Your Way
The Taste ID
A dynamic profile that interprets your style and preferences through conversation and shared content. Crate learns from expressed signals, not surveillance - creating a user model that's ethical and uniquely accurate.
From Inspiration to Action
Curated Discovery
AI agents that source, organize, and personalize content collections. We turn passive scrolling into meaningful engagement by connecting inspiration to action.
Why It Matters
Memory is Not Understanding
Most AI systems are learning how to remember. The harder problem is learning how to interpret, generalize, and stay aligned with what people actually want. Crate is built for that shift across three transitions.
Reactive → Proactive
Most AI tools wait for instructions. Crate anticipates, which requires understanding preference before the question is asked.
Memory → Understanding
Memory captures what you did. Understanding models what you want. Without interpreting signals, memory alone can’t drive relevance
Search → Prediction
Finding information is easy. Knowing what fits requires judgment, and a model of the user that improves over time.
The Vision
A personal intelligence layer
Crate's long term goal is to build the taste layer AI has been missing, so AI can understand preference the way people do
As your Taste ID evolves, it becomes a living graph of preferences and intent that helps AI rank, recommend, and plan with real personal context.
So context carries forward
Technical Blog
Insights from the team
