Flowt Secures Pre-Seed Funding to Automate Climate Finance Underwriting in Africa

The bottleneck in African climate finance is underwriting. Small climate-smart businesses generate revenue but cannot produce the data structure lenders require. Capital defaults to large energy deals where the unit economics of diligence work.

The AI platform ingests a company's own accounting and bank records, cross-verifies them, and outputs a lender-ready financial picture in days, not months. Funding came from Delta40 Fund I, Impacc, and Argidius Foundation.

The Mechanism

Flowt reads. It does not predict. Process: pull transaction data from the operator's accounting layer, run AI and machine-learning checks against bank statements, output verified cash position and repayment capacity. Result: a credit picture accurate enough to underwrite against within an afternoon rather than six months.

Effect on capital flow: cheap, accurate reading lowers the floor on deal size. If diligence cost drops, small loans become economical. Climate capital in Africa currently pools in large projects because smaller ones cost too much to assess on a per-deal basis. The software pulls a business's own transaction data, checks accounting against bank records, and produces lender-ready output. Plenty of companies attach AI to their name for the label. At Flowt, AI does the work that makes the loan possible.

First Deployment: GreenBay

First facility: GreenBay, a Kenyan circular-commerce operator. Refurbishes home appliances, solar units, and other second-life equipment for households and small businesses. Year two of operations. No corporate-credit anchors a bank could lean on.

Constraint: sales scale linearly with inventory held. Growth ceiling is working capital, not demand. More stock means more sales. Less stock means fewer.

Flowt execution: integrated with GreenBay's Odoo system, verified bank statements independently, assessed in days. Loan issued. GreenBay used it to buy and turn inventory, began repayment through a Flowt wallet. Wallet structure enforces a separate purchase-and-collections account, isolating loan cash from operating expense. Result: cleaner picture for lenders, lower risk for Flowt, predictable repayment path for the operator.

What to Track

• Pre-seed terms → disclosed Series A within 12–18 months
• Verification cost per assessment vs. average loan size (unit economics check)
• GreenBay inventory turnover post-facility (mechanism proof or anecdote)
• Geographic expansion beyond Kenya
• Lender throughput, not just borrower count