Contracts & legal docs
Extract parties, terms, obligations, and key dates. Clauses that deviate from your playbook are flagged before a human touches the file.
AI Document Processing · PXT AI
Document processing that survives bad scans.
OCR + LLM pipelines that pull structured data from contracts, invoices, loan files, and logistics forms. Including the bad scans, handwriting, and edge cases generic OCR misses. Classify. Validate. Route. Flag exceptions for humans.
−87%
doc processing time (logistics)
4h → 25m
per loan file (mortgage)
89%
straight-through rate achieved
Six document types. One pipeline. Zero silent failures.
We classify, extract, validate, and route. Documents that pass the confidence threshold go straight to your system. Documents that don't go to a human queue, not into your ERP as bad data.
Invoices & AP documents
Vendor name, line items, amounts, PO match, exceptions. Clean invoices go straight through; genuinely ambiguous ones go to a human queue.
Loan & mortgage documents
W-2s, paystubs, bank statements, and tax returns pulled from borrower email threads, parsed into your LOS, missing items flagged automatically.
Shipping & logistics docs
Bills of lading, customs declarations, proof-of-delivery scans. Including the poor-quality photos that break every off-the-shelf vendor.
Forms & structured data
Intake forms, insurance applications, medical records, survey responses. Extract fields, validate against your schema, route to the right system.
Financial statements
Balance sheets, P&Ls, bank statements. Pull totals, ratios, and trends into your spreadsheet or data warehouse with no copy-paste.
The numbers from the last two doc-pipeline builds.
Two recent builds. Both started with a prototype on real documents before a single line of production code was written.
−87%
Doc processing time · logistics SaaS
4h → 25m
File-prep per loan · mortgage brokerage
89%
Straight-through processing rate achieved
We'd written the budget for a vendor SaaS. They convinced us to spend a third of that on a prototype first. The prototype showed us the vendors couldn't handle our messy 30%, and gave us a system that could. Eighteen months in, it's still running.
VP Engineering, logistics SaaS
OCR is the starting point. LLM reasoning is the finish line.
Generic OCR returns text. Our pipelines return structured, validated, confidence-scored data, with every uncertain value routed to a human before it touches your system of record.
01
OCR is the starting point, not the finish line.
Commodity OCR works fine on clean, high-res PDFs. Your documents are not all clean, high-res PDFs. We layer LLM reasoning over the OCR output to recover context the text layer missed: rotated fields, handwriting, watermarks, low-contrast scans.
02
Humans on every exception path.
The pipeline scores its own confidence. When a field is ambiguous (a blurred total, a non-standard form layout, a handwritten annotation) it routes to a human queue rather than guessing. Your data stays accurate; your team handles the 3-10% that actually needs them.
03
Every extraction is auditable.
Every output is linked to the source region of the source document. Your team can see exactly where a value came from, dispute it in one click, and feed that correction back into the pipeline.
04
Fail loudly, never silently.
A pipeline that guesses quietly is worse than no pipeline at all. Ours surfaces uncertainty, logs confidence scores per field, and alerts a human before it commits a wrong number to your ERP or LOS.
Document pipelines are often the first step in a broader workflow. See how we connect extraction to action on our AI workflow automation page, or explore vertical builds for logistics and mortgage brokers.
Predictable budgets. Prototype first, always.
A fixed cap before we write a line of production code. Never a surprise invoice.
Document Processing Build Starts at $10,000 4-10 weeks typical
Most single-document-type pipelines run $10,000-$30,000. Complex multi-type pipelines with ERP integration fall toward the top of that range; targeted single-source extractions sit closer to the floor. We prototype on your real documents in the first two weeks so you see accuracy numbers before committing to a production build. Budget cap is quoted and agreed before we start. Never exceeded without your written sign-off.
- Two-week prototype on your actual documents before production budget is approved
- Per-field confidence scoring and exception routing baked in from day one
- Deployed in your cloud account; your data never transits our servers
- Full documentation, runbook, and eval harness on handoff
Questions before the contract.
How accurate is it on bad scans and handwriting?
Better than commodity OCR, but the honest answer is: it depends on the document type and quality floor. For logistics scans we hit 89% straight-through with 11% routed to human review. Handwriting accuracy varies by legibility. We prototype on your actual documents before committing to a production target, so you know the real number before you sign off on a build.
Can it integrate with our LOS, ERP, or document management system?
In most cases, yes. We have built integrations with Velocity, nCino, Salesforce, HubSpot, SAP, NetSuite, SharePoint, and custom databases. If your system has an API or a database we can read, we can route structured output directly into it. We scope the integration in week one.
How do you handle PII and compliance?
We deploy inside your cloud account (AWS, Azure, or GCP) when data residency matters. No documents transit a third-party server. We use enterprise API tiers with zero data retention for model calls, and we sign whatever DPA or BAA your legal team requires. PII fields can be anonymized at the extraction stage so downstream systems never see the raw values.
What happens to documents the pipeline can't read?
They go to a human queue automatically, with the reason logged. We instrument every pipeline with per-document-type confidence tracking, so you can see exactly which document categories have the highest exception rates and decide whether to retrain, add a rule, or keep them human-handled.
How is this different from using a vendor like AWS Textract or Azure Form Recognizer?
Those tools handle structured, high-quality documents well, and we actually use them as the OCR layer inside our pipelines. Where they fall short is on the messy 20-30%: non-standard layouts, low-resolution scans, handwriting, mixed-language fields, and documents that need judgment rather than pattern-matching. The LLM reasoning layer on top is what gets you from 70% to 89%+ straight-through.
Ready to clear your document queue?
Stop moving data out of PDFs by hand.
Show us a sample of your messiest documents. We'll tell you what's possible.
No deck · No demo · No sales pressure