2Q'26 · Confidential
We start with women — the world's largest underserved market.
At venture depth of engagement, one portfolio manager covers 8–12 companies; even in light-touch commercial banking, one relationship manager tops out at ~50 accounts. Either way, a 500-company portfolio costs $4–8M+/year in salaries alone. Economically impossible at $900 ticket sizes.
Serving women in Kenya, the Philippines, and Senegal simultaneously requires dozens of languages.
Debt restrains a business's flexibility and growth. Grant capital generates no return. Equity was too expensive — until AI.
Impact measurement and KPI reporting at $900 ticket sizes was economically impossible — which is itself valuable to lenders seeking evidence of financial and impact returns.
Partnerships with relevant NGOs — the charities and non-profits that already work directly with women entrepreneurs in these communities — result in very low client acquisition costs, and alignment of incentives creates a large population of clients that would be difficult for others to reach. NGO partners curate and pre-qualify candidates, have an economic incentive to refer only the strongest, and take responsibility for the ones they sponsor. This is the infrastructure that makes $900-ticket equity investing possible. NGOs are the primary sourcing channel today; commercial partners (slide 6) are the second.
A single NGO partner typically introduces 50–150 vetted candidates per year. Across 218 mapped partners, that is a pre-screened top-of-funnel of 10,000–30,000 women per year — before any marketing spend. No other capital provider in the world has this input.
NGO partnerships are how Seen Capital reaches candidates today. FMCG distributors — who already know these women commercially — are how we scale.
A woman selling Coca-Cola on the street has a supplier who knows exactly how much she buys, how often she pays, and whether her orders are growing. That supplier has been building a credit file on her for years — without knowing it.
An NGO tells you she's motivated. A distributor tells you she's ordered 12 crates a week for 18 months, never missed a payment, and grew 30% last quarter. Quantitative, continuous, directly relevant to the revenue-share model.
NGOs refer candidates because it aligns with their mission. A distributor refers candidates because funding their customers grows the distributor's own revenue. The incentive is directly economic.
Distributors already have delivery routes, sales reps, and warehouses. They visit these women weekly. That's infrastructure we don't have to build.
"You have 3,000 street vendors buying from you. Some would sell more if they had working capital. We fund them. They grow. You sell more. You give us their purchase history so we can pick the strongest candidates. No cost, no risk. Your customers get capital, you get growth."
One FMCG distributor partnership in a single geography can unlock thousands of pre-scored candidates overnight — a different growth curve from signing individual NGOs.
Each data source has a different failure mode. A candidate can game an interview but can't fake 18 months of purchase orders. She can have clean mobile money history but be declining commercially. The model's power comes from triangulation.
Behavioural signals, motivation, business plan, social context, community role. The qualitative layer — what the numbers can't tell you.
Purchase frequency, order size, payment timing, seasonal patterns, growth trajectory. Observed commercial behaviour verified by a third party with money at stake.
Income regularity, savings patterns, bill payment discipline, transaction volume, average balances. Financial behaviour via M-Pesa, MTN MoMo, GCash, or open banking.
Title-retained equipment, inventory levels, productive assets. The recovery floor that bounds the downside on every investment.
A candidate with 13 clean revenue-share payments, 18 months of growing distributor orders, and a stable mobile money balance has a default probability approaching 0.1%. That is actuarial-grade confidence — the dataset institutional capital requires before it commits.
Three established models have tried to serve small businesses in emerging markets. Each fails in a specific, structural way. Micro-equity is the first instrument that fits the economics.
Every deal is individually priced by the AI, based on what it learns during the qualification session. Two distinct return streams, with a clear order of operations.
A percentage of weekly revenue, dynamically set by the AI based on 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.
Each performing investment returns 2× via revenue share over an average 18-month repayment window. The equity stake sits on top. The benchmark for this asset class is microfinance, which averages about 5% annual return. Seen comfortably beats that even at catastrophic default scenarios.
| Scenario | Portfolio Performing | Annual Return (Revenue Share) | + Equity Upside |
|---|---|---|---|
| 5% default | 95% | ~60% /yr | 6–9% equity in ~143 businesses |
| 8% default | 92% | ~56% /yr | 6–9% equity in ~138 businesses |
| 12% default | 88% | ~51% /yr | 6–9% equity in ~132 businesses |
| 15% default | 85% | ~47% /yr | 6–9% equity in ~128 businesses |
| 30% default (catastrophic) | 70% | ~27% /yr | equity in ~105 businesses |
| Microfinance benchmark | — | ~5% /yr | none (debt instrument) |
Assumes 150 investments at $900 each, 2× revenue-share cap, 18-month average repayment duration, equity return not yet included. Microfinance literature suggests 3–8% default rates for well-screened women borrowers — AI-scored pipeline with NGO pre-qualification targets the lower end.
Even at a 30% default rate — far worse than any microfinance portfolio in history — the revenue share alone still delivers ~27% annually. That is still more than five times the microfinance benchmark. The structure makes it extraordinarily difficult to produce a mediocre return.
We built an interactive simulator. Change the default rate, the repayment duration, the ticket size, the equity exit multiple — and see what the portfolio returns in real time. Every number on the previous slide is reproducible in the tool.
Default hazard curves by payment week. Confidence intervals on default rate as data accumulates. Portfolio-level IRR under your own assumptions. Annual returns compared against the microfinance benchmark.
This is not a pitch tool. It's the same model we use internally to stress-test the thesis. You get the full model.
Open the simulator →Password in your cover email.
| Kenya | 4.50 | 18 NGOs | 3M+ women | High mobile-money penetration. Fintech sandbox. Rwanda passporting. |
| Ghana | 4.30 | 18 NGOs | 500K+ women | Foreign MFI ownership permitted. Stable democracy. Strong mobile-money rails. |
| Rwanda | 4.30 | 15 NGOs | 200K+ women | 96% women financially included. Best-in-region governance. |
| Senegal | 4.00 | 12 NGOs | 150K+ women | High mobile-money adoption. WAEMU gateway to 7 more countries. |
| Philippines | 4.05 | 10 NGOs | 100K+ women | 81M mobile-money users. BSP sandbox. English-speaking. |
Mobile-money platforms (M-Pesa in Kenya, MTN MoMo in Ghana, Wave in Senegal, GCash in the Philippines) enable digital disbursement and weekly revenue-share collection without bank infrastructure.
Platform, community, diplomacy, fund-raising, and development finance — each role held by a partner who has built it at scale before.
Serial entrepreneur. AI pioneer since 1994. Multiple exits including a London AIM listing. Former sole big-data partner to China Telecom. Built and scaled technology businesses across emerging markets for three decades.
Women on a Mission co-founder (14 years). Former worldwide executive at Estée Lauder, Bally, and Cartier. Currently founding Leparfum.ai. NGO partner relationships across six geographies.
Former US Ambassador to Egypt and the United Nations. Sovereign-government and UN-system relationships that open country-level access, multilateral partnerships, and sovereign LP pathways for Seen Capital and Fund I.
Global Head of Capital Formation and Managing Director at Fortescue Capital. Former Founding Partner at MVision Private Equity Advisers — one of the world's leading placement agents, having raised investment funds of over $175 billion for alternative asset managers. Joining Seen Capital as Fund Co-Founder and seed investor.
Former Vice Chair, Public Sector, at J.P. Morgan (through February 2026), with deep connections to the world's finance ministers, central bankers, and development institutions. Founder and Chair of J.P. Morgan's Development Finance Institution; co-founder of its Infrastructure Finance business. Former COO/EVP (#2 official) of the Inter-American Development Bank. Former Deputy Assistant Secretary at the U.S. Treasury. Joining Seen Capital as seed investor, for development finance, international expansion, and governance.
You invest now via a Simple Agreement for Future Equity. Your capital funds the first 150 investments — the data-generating portfolio. When the Series A closes, your SAFE converts automatically into shares of the management company at a better price than Series A investors pay.
Fund the first 150 investments. Prove the default rate is low enough that the 2× revenue-share cap recovers capital reliably.
Scale across 5 Tier-1 markets. Formalise NGO partnerships. Launch Fund I. Your SAFE converts here.
Successive funds deploy capital across thousands of positions. The management company (where you hold equity) earns fees and carry on all of them.
Kent Ertugrul · Founder & CEO
kent@seen.new
Private & Confidential · Not for Distribution · 2Q'26