Document AI services for the paperwork your team reads by hand
Our document AI services turn invoices, contracts and forms into structured data your systems can use, without templates. Validation rules and human review catch uncertain results before they post, while errors are still cheap to fix.
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What is document AI?
Document AI, also called intelligent document processing, uses vision and language models to read and classify documents. It then extracts structured data and validates it against existing records. Unlike template-based OCR, our document AI services handle documents they have never seen before. That includes varied layouts, scans and handwriting, with no template per format.
Template OCR breaks on the second supplier
Traditional document capture needs a template per layout. That is workable with five suppliers and impossible with four hundred. So most organizations built templates for their top few and left the long tail manual.
The long tail is usually where the cost is, and where document AI services pay off. It is also where the errors are. The unusual documents are the ones a tired person ends up reading at 4pm.
Read the document, do not match a template
Vision-language models read a document the way a person does. They find the invoice number because they understand what an invoice number is, not because it sits at fixed coordinates.
With document AI services, that removes the template problem entirely. The system handles a new supplier’s very first document, and a supplier who redesigns their invoice does not break anything.
What our document AI services include
Classification comes first: what kind of document is this? Then extraction: pull the fields that matter for that type. Then validation: do these values agree with what we already know?
In our document AI services, validation is the step that separates a useful system from a liability. An extracted invoice total that does not match the purchase order should not post silently. Instead, the system flags it and shows both numbers to a human.
Every field carries a confidence score, and thresholds are set per field. An invoice date might post automatically at 90% confidence. A bank account number, however, requires 99% and a second check, because the cost of being wrong differs enormously.
Our AI document processing uses intelligent document processing for AI document extraction, invoice data extraction and document classification AI, combining OCR and AI automation for scans and handwriting.
- Classification by document type, including multi-document PDFs split automatically
- Field extraction without templates, across layouts never seen before
- Table and line-item extraction, including tables spanning pages
- Validation against your existing records: POs, contracts, master data
- Per-field confidence scoring with configurable thresholds
- Human review queue with the source document and flagged fields highlighted
Is document AI right for your business?
Volume and variety together signal a good fit for document AI services. High volume with a single format is often solvable more cheaply with traditional capture. High variety is where AI earns its cost.
The second strong signal is downstream error cost. Sometimes a mis-keyed figure surfaces weeks later in reconciliation or in a customer dispute. In that case, the validation layer is often worth more than the extraction itself.
Document automation solutions suit teams that still re-key data from PDFs, forms and emails into their systems every day.
- Teams processing over 100 documents a week across varied layouts
- Accounts payable functions with hundreds of suppliers and no template coverage
- Claims operations handling forms, reports and correspondence together
- Legal and contract teams reviewing agreements for specific clauses
- Logistics operations reconciling bills of lading against carrier invoices
- Any process where keying errors surface expensively downstream
What document AI can do for your business
No templates to maintain
New formats work on the first document, and a redesigned supplier invoice does not break the pipeline.
Handles the long tail
The low-volume, high-variety documents that were never worth templating are exactly what this handles well.
Errors caught at entry
Validation against existing records surfaces mismatches immediately rather than in month-end reconciliation.
Confidence you can tune
Per-field thresholds so low-risk fields flow automatically while high-consequence fields always get checked.
Handwriting and poor scans
Vision models handle handwritten forms and low-quality scans substantially better than traditional OCR.
Review that takes seconds
Flagged items arrive with the source document, the extracted value and the reason for the flag already highlighted.
Problems document AI solves
Hundreds of formats, five templates
Template coverage stalled at the top suppliers. Template-free extraction covers the whole long tail.
Keying errors found at month end
Mistakes surfacing weeks later. Validation at entry catches them when correction is cheap.
Handwritten forms still manual
Paper forms nobody could automate. Vision models make them viable for the first time.
Line items ignored
Only headers captured because tables were too hard. Line-item extraction enables real matching.
Documents arriving by every route
Email, portal, post and fax. One pipeline handles all intake channels consistently.
No audit trail on data entry
No record of what the original said. Every extraction links back to the source document and page.
What every document AI engagement includes
Everything below is in scope on a standard engagement. Nothing here is an upsell discovered halfway through the build.
Multi-channel intake
Email attachments, shared folders, SFTP, scanner output, portal uploads and API submission, normalized into one pipeline.
Document classification
Type identification and automatic splitting of multi-document PDFs, so a 60-page bundle becomes correctly separated records.
Template-free extraction
Field extraction driven by understanding rather than coordinates, working on layouts never seen before.
Table and line-item capture
Structured extraction of line items including tables that span pages and inconsistent column layouts.
Validation rules engine
Cross-checking extracted values against purchase orders, contracts, master data and arithmetic consistency.
Confidence scoring and routing
Per-field confidence with configurable thresholds determining automatic posting versus human review.
Review interface
A queue showing the source document alongside extracted fields, with flagged values highlighted for fast correction.
System integration
Validated data written directly to your ERP, accounting, claims or practice management system.
Which AI models and tools do we use for document AI?
We are not tied to one vendor. Model and infrastructure choices are made on accuracy, cost per task, latency, and where your data is allowed to live.
How we deliver document AI, step by step
Every document AI engagement follows the same five stages. You always know which document types come next and how we will measure accuracy.
Discovery
First, we sample your real documents, map intake channels and audit the systems the extracted data must reach.
AI Strategy
Next, we rank document types by volume, variety and error cost, then set per-field confidence thresholds.
Pilot Build
Then we build a pipeline for the top-ranked document type as a fixed-scope pilot, with a measured accuracy report.
Implementation
After the pilot, we connect validation to your POs and master data, set up the review queue and train staff.
Optimization
Finally, we review per-field accuracy monthly, adjust thresholds and report hours and dollars saved.
How much does a document AI project cost?
Document AI services are priced on your document types, monthly volume and the systems the data goes into. You get a fixed written price before we start, and an estimate of the manual keying it removes.
Book a free 30-minute call and bring a few of the documents your team types up by hand. We will show you what can be extracted automatically and how accurate it would be.
- A written savings estimate before any paid work
- Fixed scope and fixed price, agreed up front
- Full ownership of everything we build for you
- An honest recommendation when the numbers do not work
Figures are internal measurements across recent engagements, reported to every client monthly in writing.
Document AI FAQs
How accurate is document AI?
On clean, typed documents we typically see 97 to 99% field-level accuracy. Handwritten and poor-quality scans are lower and vary considerably with source quality. Rather than quoting a general figure, we test on a sample of your actual documents during discovery. You then get a measured per-field accuracy report before you commit. Fields where accuracy is insufficient get a mandatory review gate rather than being automated.
Do we need a template for each document format?
No, and that is the main advantage over traditional capture. The system reads documents by understanding their content rather than matching coordinates. It handles a new supplier’s first document correctly, and a supplier redesigning their invoice does not break anything.
What happens with documents it cannot read confidently?
They route to a human review queue that shows the source document alongside the extracted values. It also highlights the low-confidence fields. Review typically takes seconds because the person is verifying rather than keying. Confidence thresholds are configurable per field. A bank account number can require near-certainty while a description field posts more freely.
Can it handle handwritten forms?
Yes, considerably better than traditional OCR, though accuracy depends heavily on legibility. We test against your real handwritten samples in discovery. For forms where handwriting quality is poor, the practical design is usually extraction with mandatory review rather than full automation. That still removes most of the keying effort.
How does it connect to our ERP or accounting system?
Validated data is written directly to your system through its API, or through file-based import where no API exists. Our document AI services support the common ERP, accounting, claims and practice management platforms. Where necessary, we have built connectors for legacy systems by automating their interfaces.
What does document AI cost?
It depends on the document types, volume and the systems the data feeds. We test on your own documents first, then give you a fixed written price for the build. Book a free 30-minute consultation and bring sample documents.
Ready to scope your document AI project?
Book a free 30-minute consultation. Bring one workflow. You will leave with a realistic estimate of what it would cost to automate and what it would save.