A fast-growing B2B lending fintech (named reference under NDA) was reading every loan-application package by hand before it could reach credit decisioning. We turned that into document intelligence at scale, so growth stopped meaning proportional headcount.
The lender was changing how businesses access credit, and its own growth was creating the constraint. Every loan application arrived as a package of unstructured financial documents — bank statements, company accounts, director information, supporting evidence — and every package had to be read, extracted and validated by hand before it could move into credit decisioning.
Volume was climbing, and the only lever the team had was to add people. But manual processing does not scale the way a fintech needs to: headcount grows linearly, output drifts in consistency, and a bottleneck forms at exactly the point where deal velocity matters most. The work itself was almost entirely predictable and rule-bound, and still being done by hand.
The core problem: a data-extraction workflow that was structured and repeatable, trapped inside a manual process.
We deployed an intelligent document-processing workflow built on TurfAI — ingesting each loan-application package, extracting structured data from unstructured financial documents, and routing validated outputs into the lender's existing credit-decisioning system.
The production pipeline:
Integration was via REST API directly into the existing application platform, with no downstream migration, and the workflow was live within weeks of the initial engagement.
We owned the outcome end to end, integrated into the lender's live stack, and stayed accountable through go-live rather than handing over a build and walking away. The lender scaled application volume without scaling its processing team. TurfAI owned how this was built; what we had promised the lender was owned alongside it on the BFSI side. Two named leads, both there for the length of the engagement.
Output consistency improved alongside the volume: extraction errors from manual handling reduced, and credit decisioning received standardised data rather than variable hand-keyed input. The scaling model became volume-independent, so deal velocity is no longer gated by processing capacity.
The design decision doing the work here is the confidence threshold. Anything the extraction is not sure about is flagged rather than guessed, and the reviewer receives the partial extraction with the uncertain fields already identified. That keeps a human accountable for the exceptions without putting one in front of every document.
Stack — TurfAI · REST API integration · document classification and field extraction · confidence-thresholded exception routing
Tell us the package, the volume and where it goes next. We will baseline the manual step before anyone proposes a build.
