Solution
For lending operations and intake teams: every inbound borrower document classified by content, slotted into the loan-program checklist, and flagged the day it arrives.
The problem
A 40-page scan holds three different documents; the K-1 arrives as a photographed page. Someone opens every file to find out what it actually is.
The underwriter discovers the 2024 return is actually 2022, and the deal loses a week while the borrower is chased for the right one.
Intake staff sort, rename, split, and slot documents by hand — slow on a good day, inconsistent on a busy one, and the error rate travels downstream.
The product, not a promise
How it works
Documents arrive from email, portal uploads, and scans — mixed, misnamed, and out of order.
Each document is identified by its content — a 1040 is a 1040 even when the file is called scan_final_2.pdf.
Borrower names, entities, tax years, and periods are pulled and attached to each document.
Documents slot into the intake checklist for the loan program, building the package as items arrive.
Wrong-year returns, missing pages, and unreadable files are surfaced immediately, not at underwriting.
Who it's for
Intake specialist
Head of lending operations
Compliance / internal audit
Every lending workflow inherits the quality of its intake. Borrower documents arrive as they always have — a portal upload here, an email attachment there, a 40-page scan containing three different documents — and someone has to turn that pile into an organized package before any real work starts. Done manually, it is slow and inconsistent. Done wrong, the cost lands downstream: an underwriter discovers mid-review that the “2024 return” is actually 2022, and the deal loses a week.
The platform reads every inbound document and classifies it by content, ignoring the filename. A K-1 is recognized as a K-1 whether it arrives alone, buried in a combined PDF, or as a photographed page. Multi-document files are split, duplicates are detected, and each item is stamped with the identifiers that matter: borrower, entity, tax year, statement period.
Knowing a document is a bank statement is half the job; the other half is knowing whether it is the bank statement the deal needs. Classification runs against the intake checklist for the specific loan program, so each document lands in its checklist slot and the gaps become visible in real time. Wrong-year returns, missing schedules, and stale statements are flagged the day they arrive, while the borrower conversation is still easy.
Every classification decision is auditable: what the document was identified as, what was extracted, and with what confidence. Low-confidence items route to a person instead of guessing — the platform’s job is to be reliably right or honestly unsure. The result handed to underwriting is a structured intake package where every document is what the label says it is, and can be proven so.
Objections, answered
The platform identifies each file by its content — 1M+ documents processed — including photographed pages and combined PDFs. When confidence is low it routes the document to a person rather than guessing — the design goal is reliably right or honestly unsure.
Classification runs against the intake checklist for the specific program, so each document lands in its checklist slot and the gap list reflects what this deal actually needs. A bank statement is judged as the bank statement the deal requires, or flagged as the wrong one.
Every classification decision is on record: what the document was identified as, which identifiers were extracted, at what confidence, and who reviewed anything low-confidence. Each label in the package can be proven, not just asserted.
Point it at a sample of real inbound documents and your program checklists, review how it classifies and flags them, and tune from there. Teams typically run it alongside manual intake within weeks and cut over as the flags earn trust.
Watch mixed, misnamed scans become a classified, checklist-slotted package live in the demo.
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