01
Ingestion
We pick the document up the moment it arrives. Email inbox, dropbox folder, API endpoint, scanner integration, or direct upload.
Tools
Email watchers, S3 events, Webhook receivers, custom integrations
Services > AI Document Processing
AI DOCUMENT PROCESSING
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 WE PROCESS
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?"
Invoices and bills
Fields extracted
VENDOR, AMOUNT, DATES, LINE ITEMS, TAX, PO REFERENCES
Contracts and agreements
Fields extracted
PARTIES, EFFECTIVE DATES, RENEWAL TERMS, KEY CLAUSES, OBLIGATIONS, GOVERNING LAW
Receipts and expenses
Fields extracted
MERCHANT, AMOUNT, DATE, CATEGORY, TAX, PAYMENT METHOD
Forms and applications
Fields extracted
ALL FORM FIELDS, CHECKBOX STATES, SIGNATURES PRESENT, DATES
Resumes and CVs
Fields extracted
NAME, CONTACT, EXPERIENCE, EDUCATION, SKILLS, CERTIFICATIONS
Shipping and logistics
Fields extracted
BILL OF LADING, TRACKING, ADDRESSES, ITEMS, WEIGHTS, CARRIERS
ID and KYC documents
Fields extracted
NAME, ID NUMBER, EXPIRY, ISSUING AUTHORITY, ADDRESS, AUTHENTICITY CHECKS
Custom and niche
Fields extracted
WHATEVER YOUR DOCUMENT NEEDS US TO PULL
EXTRACTION IN ACTION
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.
An annotated invoice from Acme Cloud Services Inc. to Northwind Industries. Invoice number INV-2024-184, issued March 15 2024, due April 15 2024. Three line items totaling 14820 US dollars. The JSON panel on the right shows all fields structured with matching numbered annotations from 1 through 7.
MANUAL VERSUS AUTOMATED
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.
CURRENT PROCESS
Manual entry by a human
Average total: 18 minutes per invoice
1
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
2
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
3
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
4
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
5
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)
6
File the original document
Move the PDF to the right folder. Tag it. Hope you can find it later when audit asks.
1 minute
WITH AUTOMATION
Pipeline does the data work
Average total: 35 seconds per invoice (humans approve only)
1
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
2
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
3
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
BY DEPARTMENT
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
THE PIPELINE
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.
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.
Tools
Custom classifiers, LLM zero-shot classification, fine-tuned models when volume justifies
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.
Tools
Claude Vision, OpenAI Vision, Amazon Textract, Google Document AI, Azure Document Intelligence, Unstructured.io, LlamaParse, Reducto
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.
Tools
Custom validation rules, business rule engines, human-in-the-loop platforms
05
Integration
The structured data lands where it belongs. Accounting system, ERP, CRM, internal database, or as a draft for human approval.
Tools
QuickBooks, NetSuite, Xero, Salesforce, HubSpot, custom APIs, direct database writes
ACCURACY AND VALIDATION
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 |
WHEN DOCUMENTS GET MESSY
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.
INTEGRATION
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
Accounting and finance systems
QuickBooksNetSuiteXeroSageFreshBooksWave
CRM AND SALES
CRM and sales platforms
SalesforceHubSpotPipedriveMondayCustom CRM
ERP AND OPERATIONS
ERP and ops systems
SAPOracleMicrosoft DynamicsOdooCustom ERP
DATA AND CUSTOM
Databases and custom systems
PostgresMongoDBSnowflakeBigQueryREST APIsWebhooks
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.
COMMON QUESTIONS
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.
How do we know if this is worth building?
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.
How is this different from buying an OCR tool?
Old-school OCR converts pixels to text. It does not understand what the text means. Modern document AI (which is what we build) extracts semantic structure. The model knows that 'Total Due' and 'Amount Owed' refer to the same field. It handles variation between vendors without templates. It also reasons about the document, not just transcribes it. If your existing OCR is generating accurate transcriptions but you still need humans to interpret the data, you do not have an OCR problem. You have a structuring problem.
What documents are easiest to start with?
High-volume, low-variance documents that already have clear value attached. Standard invoices for AP automation. Receipts for expense workflows. Resumes for high-volume hiring. These have well-understood schemas, measurable ROI, and forgiving error tolerance. Save the complex contracts and weird custom forms for phase two.
DURING BUILD
What teams ask once we have started, when implementation realities meet expectations.
What if we have documents that look totally different from each other?
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.
How do you handle documents where confidence is low?
Every extracted field comes with a confidence score. We set thresholds per field and per document type. Anything below threshold routes to a human review queue with the document and the proposed extraction side by side. The human confirms, corrects, or rejects. The corrections feed back into the system if you want. Most clients reach 85 to 95 percent auto-approval within the first three months of production use.
What about documents in languages other than English?
Multilingual extraction is well-supported by the major vision models (Claude, GPT-4 Vision, Gemini Vision). We test on your actual language mix during the build and tune accordingly. We have shipped Arabic, French, Spanish, Portuguese, Urdu, and German in production. Code-switching mid-document is handled. Right-to-left scripts are handled. Just tell us what languages to expect.
Can we connect this to our existing systems without a big IT lift?
Usually yes. We integrate at the API layer with all major accounting, CRM, and ERP systems. For systems without modern APIs, we do direct database writes, CSV drops to a folder, or email-with-attachment posting. The integration approach gets decided during scoping based on what your IT team is comfortable with.
AFTER LAUNCH
Operating questions once the system is live and processing real documents.
What happens when the system gets a document type we did not plan for?
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.
Who maintains the system after you build it?
You own everything. Code, prompts, schemas, integrations. If your team can maintain it, they can. Most clients keep us on a small retainer for monitoring, occasional tuning when new document types or vendors show up, and improvements as the volume scales. The retainer is optional. We will not lock you in.
What does it cost to run?
Two cost lines. Per-document processing (a few cents per page on managed vision models, less for high-volume self-hosted) and infrastructure (storage, monitoring, integration servers). For a typical AP automation deployment doing two to five thousand invoices a month, monthly run cost is usually in the low hundreds of dollars. We model the exact economics for your volume during scoping.
How do we audit what the system extracted later?
Every document, extraction, and validation step gets logged. Audit reports run on demand. For regulated industries (finance, healthcare, legal), we set up audit logs that match your existing compliance regime. You can see exactly what the system read, what it decided, and who approved it.
SHOW US A DOCUMENT
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.
