Kita

In emerging markets like the Philippines, open finance is still nascent. Most of the population is traditionally unbanked, banking APIs don’t exist, and a borrower’s financial history lives in documents: e-wallet records, bank statements, utility bills, and more. Because this data is unstructured, credit and risk teams are forced into manual review. This slows decisioning, increases costs, and caps lending volume. Legacy OCR solutions break on noisy, real-world files and still require human verification. Kita is the first document intelligence platform built specifically for lending. We are hyperlocalized around the signals that actually drive lending outcomes in emerging and undertapped domestic markets like the Philippines, Indonesia, Mexico, and beyond. Using a layered system led by vision-language models and computer vision, we outperform traditional OCR by transforming messy borrower documents into fraud-checked, decision-ready signals lenders can use directly in underwriting. Under the hood, Kita is a learning system. We link document-level signals to repayment outcomes, allowing our models to continuously improve fraud detection and risk assessment over time. This creates a compounding advantage for lenders as their distinct underwriting decisions feed back into the system. We secured a six-figure paid pilot with a Philippine lender the week we built our prototype. Since then, we’ve expanded with customers across multiple Southeast Asian markets, shipped new features continuously, and grown roughly 30% MoM. We’re Carmel and Rhea. We met before Stanford and have been building together ever since. Carmel is from Manila, is a repeat founder, and spent three years in product at Apple. Rhea has a research background in computer vision and received the highest honor in Stanford Computer Science. Together, we combine deep local context with strong technical execution to build the infrastructure that expands access to credit in emerging markets.