We help serious Indian families in Canada find marriages that last — by replacing endless profiles with deep understanding, a human matchmaker, and honest compatibility.
The global online matrimony market is ~$5.5B and growing to ~$12B by 2032. Yet the product hasn't changed in 20 years: caste, height, salary, a photo — and a search box. The hardest, highest-stakes decision in a person's life is still made on the shallowest data.
Sources: Business Research Insights (online matrimony market); Statistics Canada 2021 Census.
Second-gen singles want love and compatibility; first-gen parents want values, vetting and seriousness. Today's apps serve neither — they create conflict between the two.
Shaadi / BharatMatrimony are search portals built for India. Dil Mil and dating apps feel casual and unsafe to families. There's no premium, trusted, diaspora-native option.
For the first time, a voice AI can run a nuanced, multilingual intake conversation at near-zero marginal cost — the expensive part of real matchmaking is finally scalable.
The diaspora is large, affluent, and at marrying age, but increasingly distrustful of both arranged biodata and Western swipe culture. They want a third way.
Census figures: Statistics Canada 2021. SAM/SOM are illustrative top-down estimates for discussion — to be validated with primary research and willingness-to-pay testing.
| Shaadi / Bharat | Dil Mil | Dating apps | Haani | |
|---|---|---|---|---|
| Built for diaspora | Partial | Yes | No | Yes |
| Deep compatibility | No | No | No | Core |
| Human + counseling | Upsell | No | No | Built-in |
| Family-friendly & private | Yes | No | No | Yes |
| Aligned incentives | Subscription | Subscription | Engagement | Pay per outcome |
Incumbents monetize time on platform. We monetize outcomes. That single difference reshapes the product, the trust, and the unit economics.
👉 The phone shows the member-facing compatibility view — tap Accept.
Anyone can ask questions. The hard part — and our moat — is eliciting honest answers and structuring them into comparable data. People self-report differently to a matchmaker than to a parent. Our interview is designed around that.
Natural voice conversation in English & Punjabi (code-switching, noisy audio handled). Parents and singles can both be interviewed, separately.
An elicitation framework surfaces what people actually mean — not the audience-conditioned answer — and flags low-confidence or contradictory signals.
Free-form conversation → a 7-module compatibility schema (values, family map, lifestyle, dealbreakers, personality, and more).
Built on a documented interview-elicitation framework + a truth/disclosure data model — proprietary IP that compounds with every interview.
A three-layer model turns two interviews into one honest score — a ranker, not an oracle, that gets sharper with data and human feedback.
Hard constraints (diet, faith, relocation, family expectations) filter before anything else — no wasted introductions.
Weighted scoring across values, lifestyle, family and life goals — bilateral, taking the min of both sides' fit.
Temperament and communication style nuance the score and generate the honest "talk about this" flags.
Hard filters
Weighted similarity
Style overlay
Both sides must fit
Honest frictions
0–100 + breakdown
Matchmaker sign-off
Conversion rates illustrative — to be validated in the pilot.
All figures CAD & illustrative, for discussion. Not financial advice.
| CAD | Yr 1 | Yr 2 | Yr 3 | Yr 4 | Yr 5 |
|---|---|---|---|---|---|
| Active members ($99/yr + intros) | 1,200 | 5,000 | 13,000 | 26,000 | 42,000 |
| Revenue | $0.20M | $0.85M | $2.21M | $4.42M | $7.14M |
| Cost to serve (COGS) | ($0.10M) | ($0.41M) | ($1.06M) | ($2.12M) | ($3.43M) |
| Gross profit | $0.10M | $0.44M | $1.15M | $2.30M | $3.71M |
| Gross margin | 50% | 52% | 52% | 52% | 52% |
| Operating expenses | ($0.28M) | ($0.55M) | ($1.00M) | ($1.60M) | ($2.30M) |
| EBITDA | ($0.18M) | ($0.11M) | $0.15M | $0.70M | $1.41M |
| EBITDA margin | (90%) | (13%) | 7% | 16% | 20% |
OpEx = team (eng/product/ops, excl. matchmakers), community marketing, and G&A. Operating losses in Years 1–2 during acquisition; cash-generative from Year 3. All figures illustrative assumptions to be validated in the pilot.
AI assessment ≈ $5 each · matchmaker ≈ $60K/yr loaded, ~800 members each (AI does intake & first-pass) · payments ~3% · infra scales sub-linearly. People are the cost — and the moat.
Annual basis; intra-year timing makes the true cash trough modestly deeper, hence the buffer. Figures illustrative — not a forecast or financial advice.
Responsive Next.js app: AI interview, matches feed, why-you-match detail, conversation, profile — plus family & friends invite flows.
Voice + chat intake with profile extraction, multilingual (English & Punjabi) voice via best-in-class STT/TTS, resilient failover.
The 7-stage compatibility pipeline runs today and produces a 0–100 score with a full dimensional breakdown.
Human matchmaker behind the curtain lets us validate quality & willingness-to-pay before automating — capital-efficient learning.
Next milestone: a concierge pilot with real diaspora families to convert these assumptions into evidence.
Community events, temples/gurdwaras, parent networks, referrals — channels incumbents can't buy.
One product that satisfies both singles and parents — turning the family conflict into a feature.
Every interview & outcome sharpens the engine — a moat that widens with scale.
A capital-efficient model: ~$0.5M takes us through the concierge pilot to profitability, hardening the engine and proving the unit economics with real diaspora families.