[HN Gopher] Launch HN: Kita (YC W26) - Automate credit review in...
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Launch HN: Kita (YC W26) - Automate credit review in emerging
markets
Hey HN! We're Carmel and Rhea, the founders of Kita
(https://www.usekita.com/). We automate credit review for lenders
in emerging markets using VLMs. In many emerging markets, like the
Philippines and Mexico, credit infrastructure is weak. Open finance
is still nascent, and credit bureaus are unreliable. So to apply
for a loan, lenders rely on borrowers submitting documentation to
understand their ability to repay. A borrower can submit financial
documents, such as bank statements and payslips, in any format,
from pdfs, images of physical documents and screenshots. On top of
that, financial documents in these markets are highly
unstandardized, with no consistent templates lenders can rely on.
Existing OCR and document AI tools break on these highly variant,
messy real-world documents. Generic tools are not built for lending
workflows like verification, fraud detection, and risk extraction.
As a result, credit teams fall back on manual review, making
underwriting slower, more expensive, and more error-prone. We met
before college and stayed best friends. After graduating, Rhea
visited Carmel in the Philippines, where we heard firsthand from
fintech operators that document-based underwriting was their
biggest pain point. We started building together and tested every
OCR and document AI tool we could find. They all failed on the
messy real-world documents lenders actually receive, and even when
extraction worked, they still could not produce the structured
financial data or fraud checks lenders needed. The problem was
even bigger than we thought. Across Indonesia, Mexico, the
Philippines, South Africa, and even in the US, most of lending can
be boiled down to credit analysts looking at documents. In 2025,
13.3T was lended globally, and 90% of those transactions involved
document review. This includes in developed markets. Kita uses
VLM-based agents to parse documents, detect fraud, and extract
underwriting signals from messy financial files. Today, we support
50+ document types across PDFs, scans, photos, and screenshots. Our
pipeline enhances low-quality inputs, extracts structured financial
data, and verifies it through cross-document checks, validation
against our historical database, and market-specific fraud
detection. Our architecture's base VLM is model agnostic, and
simultaneously, we train language models finetuned to
hyperlocalized credit signals in each market, using localized
lender data - every new model improves our base layer, and every
new market makes our overall stack stronger. We link document-level
signals to repayment outcomes, allowing our models to continuously
improve fraud detection and risk assessment over time. Kita
Capture is our first document intelligence product for lenders.
We're also launching Kita Credit Agent, which automates borrower
follow-up during origination over WhatsApp and email to collect
missing documents and complete loan applications. Kita Capture is
free to try (with email signup): https://portal.usekita.com/.
Here's a quick demo: https://www.youtube.com/watch?v=4-t_UhPNAvQ.
We'd love to get feedback from the community, especially if you've
worked on document AI, fraud detection, or fintech infrastructure.
Thanks for reading!
Author : rheamalhotra1
Score : 54 points
Date : 2026-03-17 19:46 UTC (1 days ago)
| wumms wrote:
| Sidenote: unlike in Tagalog, Kita means "day-care facility for
| children" in German, so names like Kita Capture and Kita Credit
| Agent could carry unintended connotations.
| andxor wrote:
| Luckily Germany is not an emerging market :)
| fakedang wrote:
| Since when did Germany victimize itself into emerging market
| category?
|
| I would be even more worried by the fact that it resembles
| Keeta, a delivery brand owned by Meituan, which actually
| operates in a bunch of large emerging markets.
| wumms wrote:
| Germany (~5% of SMEs underfunded [0]) isn't underbanked; it's
| just bureaucratically annoying; so more of an UX problem
| (which Kita helps address) with a somewhat similar outcome.
|
| The US ("63M underbanked businesses") are already targeted:
| "Automate document review for business loan applications so
| you can fund more enterprises without scaling your back
| office." https://www.usekita.com/united-states
|
| [0] https://www.eib.org/files/publications/20230340_econ_eibi
| s_2...
| krisknez wrote:
| In Croatian, Bosnian and Serbian, kita is a derogatory word...
| iririririr wrote:
| Solution to emerging markets capital access problem is not making
| the current predatory system more efficient, but investing in
| micro credit. Which will never happen at scale because it
| generates lower returns (better to have 10 bad payers than 1000
| good payers)
| iririririr wrote:
| Also, those markets, "venture capital" usually means vertical
| lending platforms. Healthtech? nah, just credit for dental
| treatment. Edutech? nah, just credit for classes. Etc.
|
| It's a very crowded space
| jondwillis wrote:
| Would love to chat, I recently wrapped up an initial version of
| an automated real estate appraisal review app which appears to
| have some of the same technical challenges and risks.
| https://getvalara.com / jwillis@valara.net
|
| Would love to share notes. I was able to get away with landing.ai
| and some really careful schema design and multi-step workflow
| with a few agents sprinkled in at the end.
| Ratelman wrote:
| If you are eyeing the South African market - I can promise you
| granting credit here is waaaaayyy ahead of the US. There is a
| very solid credit bureau and a few of the banks are already on
| the "use AI to process docs" train. For rest of Africa - they're
| bigger on using cellphone data (see Optasia). If you want some
| insight into the market - happy to have a chat (email on profile)
| krinne wrote:
| Very interesting. This should be useful in India also, if you
| ever get around to it.
| sonink wrote:
| If your VLM based pipeline is really that good for OCR - and no
| reason to believe it cant be - why dont you just launch that as a
| product. The way I see it is that these are two separate products
| - VLM based OCR for messy documents, and automated credit review
| for developing markets.
|
| I have some experience setting up automated OCR systems for one
| of the largest fintechs in Australia - and VLM based pipelines
| can definitely give an extra edge and this is easily a very large
| TAM market. However, existing players might also be upgrading
| their systems so might not be too easy to disrupt. That being
| said, credit analysis is also a very hard problem, but I am not
| sure how much quality OCR would help here.
|
| Given what I know, I would focus on the VLM/OCR problem rather
| than the Credit scoring one.
| gustavomtoled50 wrote:
| cool idea. curious how you're handling the cold start problem
| prateeksi wrote:
| The document standardization problem you're describing maps
| closely to what we see in DeFi infrastructure, different chains,
| different data formats, no consistent standards, and existing
| tools breaking on real-world inputs. The "model agnostic base +
| market-specific fine-tuning" architecture is smart. Curious how
| you handle cases where the same lender operates across multiple
| markets with conflicting document conventions, does the model
| layer stay separate per market or do you find cross-market signal
| bleeding actually helps fraud detection?
| davflo wrote:
| Hey guys, this sounds very cool. I'd like to have a chat with you
| as a person working in anti-fraud/credit-risk data science. I
| wonder if you could generalize this to other situations like the
| one my company is facing right now (subscription based business).
| ericwebb wrote:
| As you mentioned, the format of these documents can be wildly
| different between countries. Curious, do you normalize them to
| some sort of intermediate representation before running your
| lending algorithms? How long does it take to spin up a new
| country or a new domain?
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