platoseedplatoseed
Automate credit review for lenders in emerging markets
In emerging markets, 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. Kita is the AI platform for global lending operations. We help lenders in emerging markets automate application completion, document verification, and underwriting from messy financial documents — using AI to extract fraud-checked data and localized risk signals that power faster, better credit decisions. Under the hood, Kita is a learning engine. 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’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.
Kita provides AI-powered underwriting for lenders, automating document handling, fraud checks, and credit memo generation to enable faster loan decisions, especially in emerging markets. The platform supports end-to-end underwriting from borrower intake to a decision-ready memo. It targets lenders across Southeast Asia, the United States, Latin America, and Africa with a focus on underserved segments.
Kita offers an AI-driven underwriting stack that handles borrower document intake, vision-language extraction from diverse local formats, fraud detection, and automated drafting of credit memos. The platform supports live document collection via multiple channels (e.g., WhatsApp, SMS), extracts and reconciles data at line and field level, and produces a decision-ready credit memo with sources for every claim. It includes a top-of-funnel agent experience that engages borrowers across channels, and a downstream AI Underwriter that drafts the memo and a credit officer who finalizes decisions based on policy calibration.
Who it’s for: Lenders in emerging markets including CDFIs, community banks, SMB lenders, fintechs, and banks in PH, ID, MX, ZA, US, and LATAM; organizations processing large volumes of borrower documents with local formats and higher incidence of incomplete files.
Live in production with banks and fintechs across PH, ID, MX, and US; backed by Y Combinator; mentions of 100K+ borrower files processed
Carmel is the co-founder and CEO of Kita. She is from Manila and studied Symbolic Systems & Music at Stanford. Prior to Kita, she was completing her Master’s in Computer Science. She spent three summers in audio and music product at Apple, where she was one of few interns to ever ship a feature in iOS. She was awarded by the United Nations at 16, launched the first Product Fellowship in the Philippines, and co-founded DAHA in 2022, where she took Stanford’s first marketplace from 0 to 1.
Rhea is the co-founder and CTO of Kita. Rhea completed her bachelor’s and master’s degrees in Computer Science at Stanford, and was an incoming PhD in Computer Vision and Robotics at Princeton. She received the Firestone Medal, the sole highest honor of the Stanford CS Department, for her research in 2025. Rhea joined her first research lab at age 13, worked on the COVID-19 vaccine at Pfizer at 17, and has since authored 7 papers recognized and awarded internationally.
Make document-driven decisions 70x faster, with built-in fraud checks
Kita builds vision-language models to convert noisy borrower documents into fraud-checked, decision-ready data signals for underwriting, targeting lending in emerging markets (starting in Southeast Asia) and later expanding to other underserved regions and segments. The product aims to replace manual review and OCR by offering a data layer that improves loan decisioning.
Make Banking Magical
AI operating system for commercial loans
