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Your AI
sucks.

What if it didn't?

What if it didn't?

And your customers love you and pay more?

Without Clarity

-
0%

of AI product users churn by month 3.

When your AI doesn't remember your users.

With Clarity

+
0%

conversion increase

When your AI knows why she buys.

without clarity — checkout, session 12 
{
  "user_context": null,
  "purchase_history": [],
  "preferences": "unknown",
  "recommendation": "generic_upsell",
  "checkout_conversion": "10.2%"
}
→ Generic upsell. No personalization.
with clarity — checkout, session 12 
GET /api/context/sarah_e/checkout▌

{
  "beliefs": [
    "Prefers sustainable brands
     (conf: 0.94, 8 observations)",
    "Price-sensitive on accessories,
     splurges on core (conf: 0.87, 12 obs)",
    "Responds to social proof from
     similar shoppers (conf: 0.91)"
  ],
  "sessions": 12,
  "alignment_score": 0.89,
  "predicted_intent": "high_value",
  "recommended_action": {
    "type": "personalized_bundle",
    "reasoning": "Surface eco bundle
      at 15% off with review social proof",
    "confidence": 0.91
  },
  "checkout_conversion": "86.5%"
}
→ Personalized bundle. She buys.

Right Context, Right Time.

Clarity compounds your context to maximize revenue and conversion, by providing n=1 AI personalization.

clarity api — session 1
GET /api/context/sarah_e/checkout|
{
"user_context": null
"purchase_history": []
"preferences": "unknown"
"recommendation": "generic_upsell"
}
Monthly Revenue$10K
$10K$30K$50K$78KS1S5S12S30

The Right Context Lifts Conversion

"We're up 60% in monthly app revenue because of Clarity's n=1 AI personalization."

— Tom, CEO, Relationship Psychics

Build AI that remembers,
with Clarity.

Get Early Access See Our Work →

Keep reading

↓

Your users aren’t data points.
They’re prediction engines.

60 years of neuroscience shows that minds work by maintaining beliefs, weighting confidence, and predicting outcomes. We built the API for it.

Informed by the Free Energy Principle (Friston), multi-scale cognition (Levin), and predictive processing (Clark, Seth).

sarah_e / wellness_coach_agent
// Session 1 → Session 18
// 01 — model the individual
belief: “Cardio is the key to losing weight”
confidence:
0.92→
0.54
// 02 — in their own terms
emerging: “My anxiety spikes when I skip workouts—it’s not about fitness”
confidence:
0.81
// 03 — toward their actual goals

Decouple exercise from weight goal—reframe as anxiety management. Surface nutrition separately.

alignment0.00|explore anxiety-eating patterns next session

Belief signals surface mental health patterns before the user names them

Detects goal misattribution that self-report misses entirely

How it works

00

Model the individual

What they believe, how confident they are, and how that’s changing session over session.

00

In their own terms

How they make sense of their world—not your assumptions. Behavior reveals what they truly optimize for.

00

Toward their actual goals

Is it the right goal? Is it understood? Alignment at the individual level—measured and actionable.

Belief extraction. Confidence scoring. Alignment measurement. Informed by how brains model themselves—made explicit through an API.

Read the research

How It Works

Four steps to compounding context

01

Define

Create observation contexts that map to your user touchpoints.
clarity-define.ts
1POST /api/v1/observation-contexts← define what to track
2{
3  "name": "product_usage",
4  "type": "behavioral",
5  "schema": { ... }
6}
02

Observe

Stream user events through typed observation contexts.
clarity-observe.ts
1POST /api/v1/episodes← stream events
2{
3  "user_id": "usr_abc",
4  "context": "product_usage",
5  "observations": [...]
6}
03

Score

Get objective alignment scores for every interaction.
clarity-score.ts
1GET /api/v1/alignment/usr_abc← query alignment
2
3{
4  "score": 0.872,
5  "delta": +0.034,
6  "confidence": 0.95
7}
04

Compound

Self-models grow richer with every session, every data point.
clarity-compound.ts
1GET /api/v1/self-model/usr_abc← it compounds
2
3{
4  "beliefs": 12,
5  "sessions": 47,
6  "accuracy": 0.95
7}
See how we work

What our customers say

From the blog

Research on AI personalization, self-models, and building AI that understands users

How Self-Models Work
Opinion self-models personalization

How Self-Models Work

Self-models are persistent, structured representations of what an AI product understands about each user — their goals, beliefs, constraints, and preferences.
Robert Ta's Self-Model
Robert Ta's Self-Model
Memory Is the New Moat
Opinion memory self-models

Memory Is the New Moat

Features get copied in weeks. Models get commoditized in months. But a product that remembers each user — their goals, constraints, beliefs — compounds in a way competitors can't replicate.
Robert Ta's Self-Model
Robert Ta's Self-Model
Minimum Viable Evals: Start Here
Guide evaluation ai-products

Minimum Viable Evals: Start Here

You do not need MLflow or LangSmith on day one. You need a spreadsheet and 50 test cases that map to what your users actually care about.
Robert Ta's Self-Model
Robert Ta's Self-Model
0%

revenue increase — Relationship Psychics, after 90 days with Clarity

Stop guessing.
Start compounding.

See how Clarity can turn your fragmented user data into a compounding advantage.

Get Early Access See Our Work