AI-powered matchmaking

The AI that understands
who you actually want

AIMM builds a deep psychographic model of you — values, attachment style, emotional patterns — then constructs your ideal partner from the inside out. No swiping. No guessing. Calibrated matches only.

No social login. No data sold. Ever.

psych_model.json
psych_vector
0.82
attraction_vec
0.91
attach_style
secure
behavior_vec
adaptive
confidence
0.88
Psychographic profiling· Attachment style modeling· Dream partner design· Attraction vector extraction· Contradiction detection· Behavioral adaptation· Multi-modal calibration· High-confidence matching· Psychographic profiling· Attachment style modeling· Dream partner design· Attraction vector extraction· Contradiction detection· Behavioral adaptation· Multi-modal calibration· High-confidence matching·

You say one thing.
You feel another.

Every other app takes your stated preferences at face value. AIMM detects the gap — and closes it.

What you say you want
  • "Outdoorsy, active lifestyle"
  • "Doesn't take themselves too seriously"
  • "Tall, dark hair"
  • "Career-driven"
stated_prefs.json
What you actually engage with
  • Introspective, bookish types
  • Dry humor, sharp wit
  • Unconventional looks
  • Creative, flexible schedule
behavior_signal.json

Four layers. One match.

01
🧠

Psychographic interview

An LLM conducts a natural conversation — not a quiz. It builds your psychological model: values, emotional patterns, attachment style, communication preferences, relationship expectations.

Output → psych_vector
02

Dream partner design

Structured hard-constraints (non-negotiables) combined with a soft prompt enrichment pass. The LLM fills in nuance from your psychological model — building the partner in your head you couldn't articulate.

Output → partner_profile
03

Multi-modal calibration

You react to images. AIMM extracts visual features — face geometry, style signals, social cues — building your attraction vector. Each reaction updates confidence scores across the feature distribution.

Output → attraction_vector
04

Adaptive matching

Similarity search finds candidates whose vectors align with yours. The behavior layer tracks likes, replies, ghosting, feedback — adapting your model over time. Strict on high-confidence features only.

Output → calibrated match

Built on vectors, not vibes.

Every user is a rich multi-dimensional representation — not a profile card.

user_representation.ts
type UserVector = {
  psych_vector:     float[]  // psychological model
  attraction_vector: float[]  // weighted, confidence-scored
  body_profile:     float[]  // self-perception calibration
  behavior_vector:  float[]  // adaptive real-world learning
}

type MatchScore = {
  similarity:    number          // cosine distance
  confidence:    number          // calibration quality
  fixed_features: string[]       // high-confidence only
  released:      boolean         // threshold gate
}

// The matching engine is strict on high-confidence features
// and relaxed on low-confidence ones.
const match = findMatch(user, pool, {
  threshold: 0.85,
  strategy:  "high-confidence-first"
})
🔬 Psychographic layer highest weight

LLM interview extracts values, attachment style, communication patterns. The foundation everything else is scored against.

👁 Attraction layer blind on low-conf

Image reactions → visual feature extraction → weighted distribution. Confidence scores gate which features are fixed in the match pool search.

⚖️ Relatability layer lifestyle, expectations

Life stage, goals, proximity, schedule alignment. Filters incompatibilities before vector similarity is even computed.

📈 Behavioral layer adaptive over time

Tracks real interactions — likes, replies, ghosting, feedback. Updates the model continuously so matches improve with every engagement.

Confidence-scored.
Not gut-feel.

You react. The system scores. Only high-confidence features are fixed. Low-confidence features stay flexible — letting the system broaden your real pool.

conf 0.91
fixed feature
conf 0.64
flexible
conf 0.38
ignored
conf 0.86
fixed feature
High confidence — fixed in search
Medium — weighted, not fixed
Low — released, broadens pool

No black box.
No manipulation.

  • No deterministic labels

    All outputs presented as approximations, not verdicts. You are not reduced to a score.

  • Transparent image usage

    Clear disclosure of how image analysis works and what data is retained. You control deletion at any time.

  • Diversity-aware

    Active monitoring to prevent attraction vector bias from narrowing suggestions to a single archetype. We audit for fairness.

  • Full GDPR compliance

    Data portability from day one. Export or delete your full model at any time. No data brokering, ever.

differentiation

AIMM is the only system that pairs deep psychological understanding with multi-modal attraction modeling — and uses self-knowledge as an edge: the user gets better before a single match is made.

75% of dating platforms will use AI by 2030
$13B+ global dating market in 2026
25% retention uplift in AI-enabled apps

Be first in line.

We're onboarding a limited cohort to calibrate the model with real-world data. No swiping. No algorithms you can game. Just matches that make sense.

No spam. Unsubscribe any time. GDPR-compliant.