Turn PDFs, scans, and email attachments into clean structured data
AI document processing converts the paperwork your business runs on — invoices, purchase orders, packing slips, inspection reports, drawings — into structured data your systems can use. It removes the keying: the person whose job has quietly become reading PDFs and typing what they see into the ERP, one field at a time.
Willowark builds these pipelines schema-first. We define exactly which fields matter and what valid looks like, then use layout-aware models to classify, split, and extract. Every extracted field carries a confidence score, and anything below threshold routes to a human review screen instead of flowing silently into your books.
AI & Intelligent AutomationHow the work gets done
The same way every time: scope, build, hand over.
A real pipeline is more than extraction. Documents arrive by email, upload, and scanner, get classified by type, split when one PDF holds five documents, and extracted against the schema for that type. Then validation does the work OCR cannot: line items must sum to the total, the PO number must exist in the ERP, the vendor must match the account. Failures become review tasks, not bad data.
Success is measured at two levels. Field accuracy tells you whether extraction is working; straight-through rate tells you whether the process is. Most pipelines start with human review on a large share of documents and earn their way down as validation rules and extraction quality improve against your real document mix.
Scoping begins with a sample of your actual documents, ideally a few hundred pulled from the last several months rather than the clean examples someone picked out. We sort them by type and source, note which fields feed downstream decisions, and label a held-out set that the pipeline never trains or tunes against. The main trade-off is where to set the confidence threshold. Set it high and review load stays heavy but errors are rare; set it low and more flows straight through with more risk of a wrong total posted. We usually start conservative and lower thresholds field by field as measured accuracy on your mix supports it.
What goes wrong in document pipelines is usually upstream of the model: a vendor changes its invoice template, a scanner gets reconfigured to a lower resolution, or a new document type shows up that the classifier has never seen. We design for that by monitoring accuracy and review rates per document type and source, so a template change appears as a spike on one vendor rather than a slow decay across the board. Handover includes the review interface, the schema definitions, and a runbook for adding a document type or a validation rule. Review corrections are captured, so the pipeline keeps a record of what people fixed and why.
Scope it in writing
What we agree before work starts
- Document schemas defining fields, types, and validation rules per document type
- Ingestion pipeline handling email, upload, and scan sources
Build with checkpoints
Working results, not slide decks
- Extraction service with per-field confidence scoring
- Human review interface for low-confidence and failed-validation documents
Hand over something you own
Documentation, source, and training
- Integrations pushing validated data into your ERP, accounting, or quality systems
- Accuracy reporting by document type and field
Sound familiar?
Where ai document processing earns its keep.
An AP inbox of 400 vendor invoices a month, each in a different layout, keyed by hand
Supplier certs and material test reports checked against PO requirements manually
Customer purchase orders arriving as PDFs that someone re-types into order entry
Decades of scanned job files that need to become a searchable database
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Related work
AI operating software for training operations
Components:
- Schedulers (training ops): The people running the training operation.
- Operating software (AI assistance): The operating software, with AI assistance built in.
- Cloud services (distributed): Distributed cloud services behind the application.
- Training game (learning): The training game the software connects to.
Connections:
- Schedulers to Operating software
- Operating software to Cloud services
- Operating software to Training game
A global automotive manufacturer's training operation · Automotive
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Read the case study →Common questions
Asked before every ai document processing project.
How accurate is the extraction, really?
It depends on your documents, which is why we measure on your documents rather than quoting a brochure number. Clean digital PDFs extract very well; degraded scans are harder. The system is built so accuracy is knowable — every field is scored, sampled, and reported — and low-confidence output is reviewed rather than trusted.
Can it handle scans and handwriting?
Modern vision models handle scans, skew, and stamps far better than classical OCR, and printed forms with handwritten entries usually work well. Free-form cursive is the hard case; where it appears, those fields route to review by default. Hard documents cost review time, not data quality.
Where does the extracted data go?
Wherever your process needs it: posted into the ERP or accounting system through its API, written to a database, or dropped into a validated import file. The source document stays linked to every extracted record, so an auditor can always trace a number back to the page it came from.
How much of our historical backlog can it process?
As much as you want to feed it, with the caveat that old scans are usually the hardest material. We typically run a backlog as a separate batch job with its own accuracy sampling, since the goal is often searchability rather than posting to the books, and a lower bar is acceptable. For decades of job files, the useful first pass is classification and indexing; full field extraction can follow for the document types that justify it.
Do we need to train the system on our documents first?
Not in the sense of a long labeling project. Modern vision and language models read most business documents well out of the box; what needs your input is the schema, the validation rules, and a labeled sample for measuring accuracy, which is usually a few hours of a knowledgeable person's time. Corrections captured in review improve the pipeline over time, but launch does not wait on a training phase.
Where this sits
AI Document Processing, inside a ai & intelligent automation system.
The lit component is the part of the system this service delivers; the rest is what it has to work with.
Hover or focus a component to see what it is and what it talks to. Arrow keys move between them.
Inbound documents and messages are ingested, an agent reasons with company knowledge and acts through the systems of record, and a person reviews the cases that need judgment.
Components:
- Inbound (email, PDFs, forms): The unstructured work arriving every day.
- Ingestion (extract, classify): Turns documents into structured fields with confidence scores.
- Agent (reasons, uses tools): A model with tools: it looks things up, decides, and acts — within limits you set.
- Knowledge (your docs): Company procedures and history, retrieved on demand.
- Systems of record (ERP, CRM): Where the work actually lands.
- Reviewer (exceptions): The person who sees what the agent was unsure about.
Connections:
- Inbound to Ingestion over email
- Ingestion to Agent over events
- Agent to Knowledge over REST, both directions
- Agent to Systems of record over REST
- Agent to Reviewer over handoff
Strategy. Software. Systems.
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