3.3.2DOCUMENT_PROCESSING

Zero data entry. Forever.

Invoices, contracts, applications, statements, intake forms — extracted, validated, routed and filed without a person touching them.

ACCURACY

99.4%

PROCESS_TIME

−92%

DOC_TYPES

40+

the_logic

Your team is paid to think. Not to retype.

The average ops team spends 31% of its week moving data between systems. PDFs into spreadsheets. Spreadsheets into the CRM. CRM into the billing tool. It is the most expensive copy-paste in your business.

We deploy LLM-based document agents that read, extract, validate and route. They learn your edge cases. They flag anomalies for human review. They never get tired at 4pm.

If a document type comes through more than 10 times a month, we automate it. End of story.

capabilities

01
Multi-modal extraction
PDFs, scans, photos, handwritten — accuracy holds across formats.
02
Schema-validated outputs
Every extracted field validated against your schema before being written.
03
Anomaly flagging
Edge cases get queued for human review, not silently failed.
04
CRM / ERP / accounting native
Data lands in HubSpot, NetSuite, QuickBooks — not a CSV inbox.
05
Audit trail
Every extraction logged with source doc, confidence score, and reviewer.
06
Human-in-the-loop review
Low-confidence items routed to an internal reviewer dashboard.

runtime.exec

How it actually runs.

STEP_01 · INGEST

Email, upload, or API

Docs arrive via inbox, web upload, or vendor API. All paths converge.

$ ingest --sources=email,upload,api

STEP_02 · CLASSIFY

Identify doc type

Invoice? Contract? W9? LLM tags it with confidence score.

$ classify --model=doc-classifier-v2

STEP_03 · EXTRACT

Field-level extraction

Pulls every field defined in the schema for that doc type.

$ extract --schema=invoice.v3

STEP_04 · VALIDATE

Sanity-check + cross-ref

Totals add up? Vendor on file? Date in range? Else: flag.

$ validate --rules=fin-rules-2026

STEP_05 · POST

Write to system of record

Lands in QuickBooks/NetSuite/CRM with the source doc attached.

$ post --target=quickbooks

use_cases

Where this gets deployed.

AP / invoice processing

Vendor invoices in inbox → coded and approved in your accounting tool.

Insurance applications

Submission → quote-ready data in your underwriting system.

Loan / mortgage docs

Pay stubs, bank statements, IDs all parsed and verified.

Legal contract intake

Counterparty contracts parsed for key terms, redlines, exposure.

Healthcare intake forms

Patient forms → EMR with zero front-desk transcription.

Real estate transactions

Disclosures, appraisals, title docs — extracted and indexed.

case_study

COMMERCIAL_FINANCE

14-hour AP cycle compressed to 23 minutes.

B2B lender was processing 1,200 invoices/month manually. We deployed a doc agent on AP, then expanded to KYC and loan-app intake. Three FTEs were redeployed to higher-value work. Founder bought them all dinner.

AP_CYCLE

14h → 23m

ACCURACY

99.6%

COST_PER_DOC

−87%

FTE_RECOVERED

3.2

next_step