The AI that understands who you actually want. Psychographic-first matchmaking — no swiping, no noise, only calibrated matches.
The average user spends 5.2 hours/week on dating apps and goes on fewer than 2 first dates per month. The swipe mechanic optimises for engagement, not relationships — creating a system where the app wins when users stay stuck.
The deeper problem: users don't know what they actually want. Stated preferences diverge from real attraction patterns. No existing app detects or resolves this gap.
A fully autonomous AI matchmaking platform. AIMM builds a deep psychographic model through conversation, constructs your ideal partner from the inside out, calibrates your real attraction patterns through multi-modal signals, and delivers high-confidence matches — no swiping required.
LLM conversation builds your values, attachment style, emotional model
Hard constraints + soft enrichment. The partner in your head, structured.
React to images → extract attraction vector, score by confidence
Vector similarity + behavioral signals. Improves with every interaction.
Every user is a multi-dimensional vector representation. Matching runs on cosine similarity across psychographic, attraction, and behavioral dimensions — not collaborative filtering on swipe history.
The engine is strict on high-confidence features and releases low-confidence ones — preventing the system from narrowing the pool to a single archetype.
The global dating market is growing at 11.76% CAGR. AI adoption in dating jumped 333% year-over-year. 75% of platforms will use AI by 2030. No player owns the psychographic-first category at consumer scale.
Phase 1 is a paid-only subscription — no free tier. Every onboard requires LLM inference and image generation; a money-back guarantee handles acquisition risk. Phase 2 licenses the matching engine to third parties — white-label apps, enterprise HR, relationship platforms.
Incumbents are adding AI to swipe products. AIMM replaces the swipe product entirely. The moat deepens with every user — proprietary psychographic training data, calibrated attraction vectors, and behavioral history cannot be replicated by a chatbot bolted onto a feed.
AIMM's core user has already churned from swipe apps and is actively searching for a better solution. Our GTM is built on capturing high-intent organic search, a viral referral mechanic, and editorial credibility — not paid acquisition.
Psychographic model calibration, no matching yet
Live matching, referral discount ($20 off), 7-day money-back guarantee
Paid tiers live, press push, success stories
40K users, Series A preparation
Seed funds 18 months of runway to prove the core metrics needed for Series A: product-market fit, LTV:CAC, and first revenue cohort.
Team hired: CTO, lead engineer, head of design. Psychographic model development begins.
Invite-only, NYC + London. Psychographic model NPS target ≥ 70.
Match acceptance rate ≥ 60%. Money-back guarantee active.
TechCrunch / Tier 1 press feature. Referral programme active.
LTV:CAC ≥ 5x proven. B2B API first contract. International pilots.
AIMM requires a rare intersection: consumer product instinct, LLM/vector systems expertise, and deep psychology knowledge. The founding team bridges all three.
Consumer product leadership, prior startup exit or Tier 1 PM. Relationship-conscious brand builder.
LLM fine-tuning, vector embeddings, pgvector. Built semantic search at scale.
Relationship psychology PhD. Attachment theory research. Model accuracy guardian.
18 months of runway. Product built, first paying cohort validated, Series A metrics achieved.