Invoices and bills
VENDOR, AMOUNT, DATES, LINE ITEMS, TAX, PO REFERENCES
Documents in. Data out.
Invoices, contracts, receipts, forms, claims, and the long tail of paper your team still keys in by hand. We build pipelines that read documents at machine speed, validate the extraction, and post the data where it should live.
What Is Intelligent Document Processing?
Intelligent document processing (IDP) uses AI to read documents, understand what the text means, and extract structured data. This goes beyond old-school OCR, which only transcribes pixels to text without understanding context. Modern document AI handles invoices, contracts, forms, and scans with varying layouts, extracting fields like amounts, dates, and parties automatically, then validates what it found before anything reaches your systems.
Most paper your team still touches, we can read.
Eight document categories we ship most often. The list is not exhaustive. If you have a specific document type, ask. Most of what we do starts with someone showing us a sample and asking "can you handle this?"
Contracts and agreements
PARTIES, EFFECTIVE DATES, RENEWAL TERMS, KEY CLAUSES, OBLIGATIONS, GOVERNING LAW
Receipts and expenses
MERCHANT, AMOUNT, DATE, CATEGORY, TAX, PAYMENT METHOD
Forms and applications
ALL FORM FIELDS, CHECKBOX STATES, SIGNATURES PRESENT, DATES
Resumes and CVs
NAME, CONTACT, EXPERIENCE, EDUCATION, SKILLS, CERTIFICATIONS
Shipping and logistics
BILL OF LADING, TRACKING, ADDRESSES, ITEMS, WEIGHTS, CARRIERS
ID and KYC documents
NAME, ID NUMBER, EXPIRY, ISSUING AUTHORITY, ADDRESS, AUTHENTICITY CHECKS
Custom and niche
WHATEVER YOUR DOCUMENT NEEDS US TO PULL
What happens when a real document goes through the system.
A sample invoice processed end to end. Every highlighted field on the left becomes a structured value on the right. The numbered annotations show what gets extracted, validated, and posted to your downstream systems.
Acme Cloud Services Inc.
1 Market Street, Suite 400, SF
INVOICE
INV-20481
Bill toNorthwind Industries
Date issuedMar 14, 2026
| Line items | Amount |
|---|---|
| Cloud hosting — Pro tier | $2,400.00 |
| API usage overage | $1,180.00 |
| Priority support add-on | $700.00 |
Total due$4,280.00
Fields extracted
VendorAcme Cloud Services Inc.
Invoice #INV-20481
DateMar 14, 2026
Amount Due$4,280.00
Line Items3 items
TermsNet 30
✓ Validated · posted to QuickBooks
Sample output. Real schemas are designed per customer.
The same invoice, through both processes.
A single invoice through your current process and through an automated one. The point is not the speed difference (though it is significant). The point is what your team gets to do with the time you give back.
Manual entry by a human
Average total: 18 minutes per invoice
Step
Invoice arrives in AP inbox
Email attachment, scanned PDF, or photo from a mobile device. Sometimes the file is named usefully. Sometimes it is 'IMG_4829.pdf.' (30 seconds)
Step
Open and read
Open the file, find the vendor, find the amount, find the dates, find the line items. Cross-check against the PO if one exists. (3 minutes)
Step
Key into accounting system
Type each field into QuickBooks, NetSuite, or whatever. Tab between fields. Hope you do not transpose a digit on the amount. (6 minutes)
Step
Validate against PO and approval policy
Check the PO number matches. Check the amount is within the approved range. Check the vendor is set up correctly. (4 minutes)
Step
Route for approval
Send an email or Slack message to whoever needs to approve. Wait for them to respond. Follow up if they do not. (3 to 5 days, mostly waiting)
Step
File the original document
Move the PDF to the right folder. Tag it. Hope you can find it later when audit asks. (1 minute)
Pipeline does the data work
Average total: 35 seconds per invoice (humans approve only)
Step
Invoice arrives, pipeline picks it up
Inbox monitor, dropbox folder, or API ingestion catches the document the moment it arrives. No manual upload. (5 seconds)
Step
Extract, validate, post
Document gets classified, extracted, validated against your business rules, matched to PO if available, and posted to your accounting system as a draft bill. All in one pipeline run. (20 to 30 seconds)
Step
Human reviews and approves
An approver gets a notification with the pre-filled draft. They glance at it, confirm or reject. The human stays in the loop on the decision, not the data entry. (Approval cadence depends on you)
Who already has documents stacking up.
Most document processing engagements start with one team and expand. The patterns repeat across companies. Here is where we usually start.
Finance and Accounting
Where the document volume is biggest and the ROI shows up first.
- Invoice extraction and posting to QuickBooks, NetSuite, or Xero
- Receipt and expense report processing
- Purchase order matching and three-way reconciliation
- Bank statement reconciliation for high-volume accounts
- Vendor onboarding from W-9 or W-8 forms
Legal and Contracts
Reading agreements at speed without missing the clauses that matter.
- Contract metadata extraction (parties, terms, dates, renewal)
- Clause detection (data residency, indemnity, termination)
- Mass NDA review for portfolio companies or M and A
- Lease abstraction for property and real estate teams
- Vendor agreement comparison against your standard template
HR and Talent
Onboarding, hiring, and employee paperwork without the keyboard time.
- Resume parsing and structured data for ATS
- I-9 and Right-to-Work form processing
- Onboarding document intake (passport, ID, tax forms)
- Performance review and 360 feedback aggregation
- Background check report normalization
Operations and Supply Chain
Shipping, procurement, and logistics paperwork at scale.
- Bill of lading extraction and matching
- Customs and shipping document processing
- Purchase order intake from suppliers
- Quality and inspection report digitization
- Inventory document reconciliation
Five stages from document to data.
Every document moves through the same pipeline. The components inside each stage swap by use case, but the stages do not. We pick the right tool per stage rather than locking into one platform that does all five.
Step 01
Ingestion
We pick the document up the moment it arrives. Email inbox, dropbox folder, API endpoint, scanner integration, or direct upload.
- Email watchers, S3 events, Webhook receivers, custom integrations
Step 02
Classification
What kind of document is this? Invoice, receipt, contract, form. The pipeline routes to the right extractor based on what we are looking at.
- Custom classifiers, LLM zero-shot classification, fine-tuned models when volume justifies
Step 03
Extraction
Pull the structured fields. We use vision-capable language models for documents with variation, dedicated extractors for high-volume standardized forms, and OCR fallbacks when needed.
- Claude Vision, OpenAI Vision, Amazon Textract, Google Document AI, Azure Document Intelligence, Unstructured.io, LlamaParse, Reducto
Step 04
Validation
Does the extracted data make sense? Amounts within range, dates plausible, fields cross-referenced against your existing systems, required fields present. Anything ambiguous goes to a human.
- Custom validation rules, business rule engines, human-in-the-loop platforms
Step 05
Integration
The structured data lands where it belongs. Accounting system, ERP, CRM, internal database, or as a draft for human approval.
- QuickBooks, NetSuite, Xero, Salesforce, HubSpot, custom APIs, direct database writes
Where the system shines, and where it asks a human.
Document AI is not magic. Standardized documents extract reliably. Phone photos of crumpled handwritten receipts do not. The right architecture handles both, by knowing what to send to the model and what to escalate. Below is what we see at typical production scale, plus how we handle the messy half.
| Document Type | Typical Accuracy | Validation Method | Human Review |
|---|---|---|---|
| Standardized invoices and receipts | 97 to 99 percent | Auto-match against PO, vendor whitelist, amount ranges | Only on validation failure |
| Contracts and legal documents | 92 to 96 percent on structured fields | Field consistency checks, key clause presence | Always (for legal review) |
| Resumes and CVs | 94 to 98 percent on standard fields | Schema validation, contact verification | Optional, depends on ATS configuration |
| Handwritten or low-quality scans | 80 to 92 percent (variable) | Confidence scoring, dual-extractor agreement | Default to human review below confidence threshold |
| Custom and niche documents | Depends on training data | Custom rules per document type | Tuned per use case |
Four patterns that defeat most off-the-shelf tools.
Phone photos with skewed angles, glare, and shadows.
We use vision models with built-in image normalization and routing logic that detects low-quality input early. If the photo is unreadable, the pipeline asks the user to resubmit instead of pretending it can extract from it.
Handwritten fields in otherwise typed forms.
Mixed-modal documents (typed labels with handwritten values) are common in onboarding, healthcare, and legal. We route the handwritten regions through models specifically tuned for handwriting, then merge results with the typed extraction.
Multi-language documents and code switching.
Invoices in Arabic and English, contracts in French and Spanish, resumes that switch languages mid-sentence. We pick extractors that handle the source language natively rather than translating first, which loses precision.
Documents with structural variation.
Two vendors send invoices in completely different layouts. We do not write a separate template for each one. The vision-capable models extract semantically, by what a field means, not by where it sits on the page.
The data goes where it needs to live.
Structured data only matters when it lands in the systems your team already uses. We integrate directly. No intermediate spreadsheets or CSV exports unless you specifically want one.
Accounting and finance systems
- QuickBooks
- NetSuite
- Xero
- Sage
- FreshBooks
- Wave
CRM and sales platforms
- Salesforce
- HubSpot
- Pipedrive
- Monday
- Custom CRM
ERP and ops systems
- SAP
- Oracle
- Microsoft Dynamics
- Odoo
- Custom ERP
Databases and custom systems
- Postgres
- MongoDB
- Snowflake
- BigQuery
- REST APIs
- Webhooks
If you need the data somewhere else (a CSV in a folder, a row in a spreadsheet, a notification in Slack), we can do that too. The pipeline does not care where the data goes. It just needs to know.
Different Page If
- You need a searchable knowledge base over your existing documents, not extraction into structured fields: RAG & Knowledge Base Systems
- You need a customer-facing chatbot, not a back-office pipeline: Conversational AI Chat Bots
- You need LLMs wired directly into your product APIs.
- You need autonomous multi-step agents across multiple tools: AI Agents and Agentic Workflows
Questions, grouped by when they actually come up.
Same person, different concerns at different stages. Below are the questions we get most often, organized by where they tend to surface in the conversation.
Before Scoping
The questions buyers ask before they have even decided if document processing is the right approach.
Math. Count the documents your team handles per month. Estimate the average time per document. Multiply. Compare against a build that runs four to twelve weeks and a per-document run cost that is usually pennies. Most teams crossing 500 documents a month see payback inside six months. Below that volume, the math is less obvious and we will tell you so.
During Build
What teams ask once we have started, when implementation realities meet expectations.
That is the normal case, not the exception. Different vendors, different formats, different layouts. Modern extractors handle structural variation through semantic understanding rather than template matching. You do not need a separate template per vendor. You need one good extractor that understands what an invoice is.
After Launch
Operating questions once the system is live and processing real documents.
It either classifies the document as 'unknown type' and routes to a human review queue, or it attempts a generic extraction with low confidence and lets the human decide. Either way, nothing gets silently mishandled. Most new document types are easy to add to the pipeline after launch. Send us a sample and we will tell you the lift.
Most engagements start with one document type and one team. Send us a sample.
Forty-five minutes. Bring a few sample documents you wish your team did not have to type into a system manually. We will look at the volume, the variation, and the integration points, then tell you what an automation looks like, what it would cost to build and run, and whether the math actually works for your scale.
No pressure. Just value.