Review financial statements automatically
Label parses every transaction, categorizes them, and infers the signals underwriters actually care about. All in ~1 min
Label parses every transaction, categorizes them, and infers the signals underwriters actually care about. All in ~1 min
See how it works
Inputs
PDF, Plaid, ID docs
Parse
Extract & normalize
Analyze
Categorize & infer
Decision
Underwriter dashboard
Inputs
PDF statements, Plaid, ID docs
Parse
Extract & normalize
Analyze
Categorize & infer
Decision
Underwriter dashboard
These are signals that used to take hours of manual review to extract.
Every line item bucketed — revenue, payroll, rent, debt service, owner draws, transfers, processor settlements.
Recurring outflows are matched to known MCA, term-loan, and revenue-share signatures.
Monthly revenue, expenses, free cash flow, debt service coverage, and month-to-month trends.
Round-number deposits, balance manipulation, doc tampering, statement splicing.
Three months of cash in, cash out — derived from the categorized ledger. The gap is what an underwriter ultimately prices against.
Jan
Feb
Mar
Rolled up, ranked, and ready to sign off — or kicked to a reviewer when the file doesn't read clean.
Categorized ledger
90 daysEvery transaction in the period, mapped to the lender's chart of accounts.
Existing positions
Recurring outflows matched against known funder signatures.
MCA — daily ACH
Counterparty A
$412 / day
high
MCA — weekly
Counterparty B
$2,150 / wk
high
Term loan
Regional bank
$1,840 / mo
medium
Flags
Surfaced for reviewer attention. Never a silent rejection.
$5,000.00 on day 3 — no corresponding invoice trail
Reconciliation gap of $812 between Feb close and Mar open
First-seen pull from "MERCH SVCS LLC" mid-cycle
A 30-minute walkthrough on real submissions. We score them alongside your underwriters and you keep the output.