AI-native equity infrastructure for the world's largest underserved market.
March 2026 · Confidential
740 million women work in the informal economy. The MSME financing gap for women-owned businesses is $1.9 trillion — over 7% of developing-world GDP. This market has been structurally unreachable. Until now.
A human PM manages 8–12 companies. A 500-company portfolio needs 40–60 professionals at $8M+/year.
Serving women in Kyrgyzstan, India, and Nepal simultaneously requires dozens of languages.
Microfinance created dependency through debt. Grant capital generated no return. Equity was too expensive.
Impact measurement, KPI reporting, and compliance infrastructure at $900 ticket sizes was impossible.
The pipeline processes candidates in any language, at any scale. A small team oversees the entire portfolio — the AI handles intake, diligence, scoring, legal, and monitoring autonomously. The constraint is no longer people. It is capital.
Everything flows through the same AI pipeline. The platform, the distribution network, and the fund are interdependent — each one makes the others possible.
Every deal is individually priced by the AI, based on what it learns during the qualification session. The terms reflect the specific risk, revenue potential, and context of each business — no two deals are the same.
A percentage of weekly revenue, dynamically set by the AI based on the qualification session — business type, revenue seasonality, local market conditions, and risk profile.
Capped at 2× the working capital deployed.
Once the cap is hit, the revenue share stops. The woman keeps 100% of her income from that point forward.
Each investment includes a permanent equity stake in the newly formalised business — typically 6–9%, individually set by the AI.
Equity percentage never changes.
As the business grows, so does the value of the stake. Across thousands of positions, the portfolio compounds — a venture-scale equity book built one woman at a time.
Microfinance tried debt. It created dependency and consumed value through interest. Grant capital generated no return. Equity was too expensive — until AI made it possible.
| Kenya | 4.50 | 18 NGOs | 3M+ women | M-Pesa 82%. Fintech sandbox. Rwanda passporting. |
| Ghana | 4.30 | 18 NGOs | 500K+ women | MTN MoMo. Foreign MFI ownership. Stable democracy. |
| Rwanda | 4.30 | 15 NGOs | 200K+ women | 96% women financially included. Best governance. |
| Senegal | 4.00 | 12 NGOs | 150K+ women | Wave 75%. WAEMU gateway to 7 more countries. |
| Philippines | 4.05 | 10 NGOs | 100K+ women | GCash 81M users. BSP sandbox. English-speaking. |
Traditional microfinance applies one-size-fits-all criteria to every applicant. AI makes it possible to conduct deeply individualised due diligence on every single candidate — at a level of nuance no human team could sustain across thousands of investments.
Behavioural trust: mobile money history, social graph density, peer endorsement. Asset coverage: title-retained equipment as enforcement mechanism. Conversational signals: hesitation patterns, revenue confidence, community context. Each candidate scored across five dimensions with 100-point precision.
Every candidate gets a bespoke evaluation — in her language, adapted to her context, probing the specific risks her profile raises. The score updates monthly, not annually. The model learns from every outcome. By Month 18, the risk architecture has been calibrated across 500+ real investments.
Deploy first real investments. Prove the AI pipeline works. Generate performance data that de-risks the Series A.
Convertible note into mgmt co equity.
Scale platform across 5 Tier 1 markets. Formalise all NGO partnerships. Build team. Launch Fund I.
Equity in technology company.
Armed with seed + Series A data, launch successive funds.
The Law of Large Numbers: thousands of $500–$3,200 positions, each with bounded downside. The risk-reward equation changes fundamentally when you can make this many bets with this much data.
Weekly revenue share payments generate 7,800 data points per year from 150 companies. Every critical claim has a defined sample size, a confidence level, and a specific week by which the evidence arrives.
By the time the Series A pitch begins at Month 6, the portfolio has generated 2,876 payment events — enough for 99.5% confidence on the revenue model, 95% on formalisation, and a default picture that is 95% complete. At 50,000 events, the data becomes a product: credit-ratable, securitisable, licensable.
The pipeline that manages 500 companies in Fund I manages 5,000 in Fund III at comparable marginal cost. These are not fund-specific assets. That is a venture-scale story.
Stripe built the infrastructure that made payments trivially easy. Seen Capital is building the infrastructure that makes equity investment trivially deployable to anyone, anywhere.
Seen Capital is a new company. But its co-founder has spent fourteen years leading Women on a Mission — an international women's empowerment charity with alumni across six continents.
Rwanda · Nepal · Kyrgyzstan · Namibia · Ethiopia · Indonesia
Serial entrepreneur. AI pioneer since 1994. Multiple exits incl. London AIM listing. China Telecom's sole big data partner. Built and scaled technology businesses across emerging markets.
Women on a Mission co-founder (14 years). Former worldwide exec at Estée Lauder, Bally, Cartier. Currently founding Leparfum.ai. NGO partner relationships across 6 geographies.
Former senior diplomat. Career at the highest levels of US foreign policy and the multilateral development system. Deep relationships across DFIs that constitute Tranche 1 of the fund. Bio to follow.
The fund invests $500–$3,200 per company across thousands of positions. Each bet has bounded downside (maximum loss = investment size) and demonstrated upside (8.6× weighted average gross multiple across illustrative portfolio). At scale, the Law of Large Numbers turns this into a predictable return profile.
The demo is available now.
Seven agents. Any language. End to end —
intake to funded portfolio company.
Private & Confidential · Not for Distribution · March 2026