Dating apps match profiles. We model human interaction.
Seed Round · $2M Matchmaking / Relational AI Confidential · 2026
$13.1B Market size
$816K Y1 ARR target
$18.5M Y3 ARR target
~$85 Blended ARPU/mo
15:1 LTV : CAC
Problem & Solution
The problem
Dating apps are broken at the architecture level.

The swipe mechanic optimises for engagement, not relationships. Users spend 5+ hours/week on apps and go on fewer than 2 first dates per month. Worse: users don't know what they actually want — stated preferences diverge from real attraction patterns, and no platform detects or resolves this gap.

Elite matchmaking agencies solve the quality problem but charge $30K–$150K/year — accessible to fewer than 0.1% of the market.

The solution
AIMM. A fit-based matching engine.
  • Adaptive Dialogue builds the relational model — values, attachment, communication, conflict behaviour
  • Scenario Response reveals instinctive behaviour under pressure — what you do, not what you'd say
  • Visual Calibration extracts real attraction patterns from image reactions — bypasses stated preference bias
  • Presence Reading reads style, presentation intent, and physical presence from user photos
  • Fit(A, B) — not similarity. Complementarity + alignment + tolerance + adaptability.
Market & Positioning
Market sizing
TAM
$13.1B
SAM
$2.8B
SOM
$18.5M Y3
11.76%Market CAGR
0Category owners in fit-based
Why now
  • LLM quality is now sufficient for reliable psychographic interviews at consumer cost
  • Vector DBs (pgvector, Pinecone) commoditised — semantic search at scale is a startup problem
  • Therapy culture mainstream in 25–40 demo — users ready to engage with a psych model
  • Swipe fatigue is a documented, growing crisis — 40%+ user frustration
  • Incumbents cannot replace the swipe mechanic without killing engagement metrics
Defensible moat
  • Proprietary psychographic training data accumulates with every user
  • Calibrated attraction vectors cannot be reverse-engineered from swipe history
  • Behavioral adaptation history is non-portable — switching cost is the model itself
  • Incumbents adding AI to swipe products are solving the wrong problem
Pricing
Three paid tiers — no free tier
Premium $49/mo Core AI matching, distance filter, stated income range
Elite $149/mo + Plaid income verified badge, GPS confirmed, national pool, background check
Concierge $499/mo + Passport/ID scan, certified badge, HNW-only pool, dedicated matchmaker — vs $30K–$45K/year at Kelleher
Financials & Technology
Revenue projections
2026 800 users · $85 ARPU · US + UK $816K
2027 4,200 users · $90 ARPU · + CA, AU $4.54M
2028 14,500 users + B2B API · + EU, APAC $18.5M
~78%Gross margin
15:1LTV:CAC
6%Churn target
Tech stack
  • Psychographic layer — LLM interview (GPT-4o / Claude), structured output, psych vector embedding
  • Attraction layer — image generation + reaction → visual feature extraction → confidence-scored vector
  • Vector matching — pgvector / Pinecone, cosine similarity, high-confidence-first search
  • Identity verification — Stripe Identity ($1.50/check), Plaid income tier, Checkr background
  • Behavioral layer — real-time model updates from interactions, ghosting signals, feedback
Go-to-Market & Phase 2
Acquisition
  • 35% Organic / SEO — high-intent search ("why do I keep dating the wrong person")
  • 28% Referral — inviting a friend improves your own match quality (genuine mechanic)
  • 18% Press — dual angle: AI/tech + psychology/lifestyle. Target TechCrunch + Vogue
  • 12% Podcast sponsorships — relationship psychology niche, high intent
  • 7% Paid social — minimal Y1, scaled in Y2 once CAC validated
Phase 2 — B2B API

License the psychographic matching engine to third parties who need compatibility infrastructure without building it themselves.

  • Niche dating apps (faith, expat, professional)
  • Relationship coaching platforms
  • HR / co-founder matching tools
  • White-label matchmaking services
$2MB2B ARR Y3 target
The Ask
$2M
Seed Round · 18 months runway
  • $900K Engineering & product — matching engine, mobile apps, infra
  • $350K AI/ML research — fit model accuracy, bias auditing
  • $350K Marketing & growth — waitlist launch, press, referral
  • $250K Operations & legal — GDPR, entity, advisors
  • $150K Runway reserve — Series A bridge
M+18Series A prep
$4M+ARR target
60%+Match acceptance