Use cases by function9 use cases
Document processing that knows which fields it is unsure about
The enterprise name for this is intelligent document processing. The older generation of tools worked from templates: one layout, one template, and a new supplier meant a new template. Model-based extraction reads layouts it has never seen, which solves the template problem and introduces a new one. Models make confident errors, and a confident error in an invoice total or a court deadline is worse than a blank field.
So every build here is designed around uncertainty. Each field carries a confidence and a location on the page, validation rules check what can be checked (totals, dates, VAT IDs, master data), and a review screen shows a person the document region next to the extracted value for anything that fails. Corrections are stored and become the test set, so the pipeline is measured on your documents rather than on a benchmark.
The documents differ by industry, and so do the rules around them: delivery notes and forms into spreadsheets, contracts against your own playbook, commercial leases into critical dates, customs declarations for a broker to sign, clinic referrals and edited bank statements caught before they cost money.
Document processing systems I can build
Catching edited bank statements, payslips and invoices before they cost you money
Cross-checks uploaded statements, payslips and invoices against bank data, VAT records and the file's own structure, then puts the evidence in front of a reviewer.
Upload portal / Open banking provider / EU VIES / KYC provider (Onfido, IDnow or similar) / CRM or loan system
Commercial insurance submissions assembled from proposal forms, loss runs and property schedules
Reads a client's proposal forms, loss runs and property schedules, reconciles them, and drafts one submission per insurer for the account handler to check and send.
Broker management system (Acturis, Applied Epic, Vertafore) / Outlook / Excel schedules of values / Insurer portals and e-trading / BiPRO interfaces (Germany)
PDFs into rows: document data entry that sends only the doubtful fields to a person
Reads delivery notes, proofs of delivery and forms from an inbox or folder, checks each field against your TMS or ERP, and asks a person only about doubtful values.
Gmail and Google Drive / Excel and Google Sheets / TMS or ERP / OCR or vision model / Review screen
Commercial leases turned into key dates and clauses, with a lawyer checking the ones that matter
Turns a portfolio of commercial leases and their amendments into cited abstracts and computed deadlines in Yardi or MRI, with a lawyer confirming breaks and options.
SharePoint / Yardi Voyager or MRI / Excel / Critical-dates calendar / Index publishers (Destatis, CBS, ONS)
Contract review against your own playbook, with every flag tied to the clause that caused it
Checks each clause of a customer's DPA or NDA against your playbook, quotes the text behind every flag, and drafts tracked changes that a lawyer accepts or rejects.
Microsoft Word and SharePoint / iManage or NetDocuments / Clio / Ironclad or Juro, where a CLM exists / The playbook and clause library
Court mail read, deadlines calculated by rules, and every one confirmed by a lawyer
Reads incoming court documents, calculates each deadline with tested rules rather than a model, proposes entries in your case system and waits for a lawyer to confirm.
beA or e-filing notices / RA-MICRO, Advoware or DATEV Anwalt / Clio, LEAP or Actionstep / Outlook / Holiday and rules calendar
Customs declarations drafted from commercial invoices and packing lists, for the declarant to sign
Cross-checks shipment documents, reuses tariff codes from your accepted history, argues new ones with EU rulings, and drafts declarations for a declarant to sign.
Customs software (CargoWise, AEB, DAKOSY or similar) / ATLAS (Germany) or CDS (UK) / Outlook / Excel / EU TARIC and EBTI
Mortgage applications where the documents chase themselves and the underwriter gets a checked file
Keeps a live checklist per borrower, chases what is missing on their preferred channel, reads and cross-checks each document, and gives the adviser a clean file.
Broker CRM or loan origination system (Acre, Mortgage Brain, Europace, Encompass) / WhatsApp Business Platform / Gmail / Upload link / Open banking provider
Referral faxes and letters turned into patient records, with missing details chased automatically
Reads each incoming referral, checks it against what the exam needs, links the right patient and chases the referrer for gaps; clinicians keep every clinical call.
Fax-to-email and practice inboxes / KIM (Kommunikation im Medizinwesen) / RIS or practice management system / Scheduling / EU-hosted extraction and review service
What separates a document pipeline you can trust
Citations for every field
Each extracted value points to the page and region it came from. A reviewer checks in seconds, and an auditor can trace any number back to its source.
Validation beats confidence scores
A model's own confidence is a weak signal. Line items that add up to the total, a VAT ID that exists, a supplier that matches the master data: those are strong ones.
The review screen is the product
Whether a team saves time depends on how fast a person can confirm or correct a flagged field. That screen gets as much design attention as the extraction.
Documents can attack the model
Hidden text in a PDF can carry instructions aimed at an AI reviewer. Extraction runs without tools that can act, and nothing in a document is treated as an instruction.
Some outputs need a licensed signature
Customs entries, court deadlines and credit files are prepared by the system and signed by the broker, lawyer or underwriter. The design makes that review fast rather than skippable.
Where the data may go
Health, legal and financial documents often cannot leave your infrastructure. Self-hosted OCR and open models are now good enough for many of these pipelines.
Frequently asked questions
Can AI read PDFs and extract data into Excel?
Yes. Current models read scanned and digital PDFs, including tables and handwriting of reasonable quality, and can fill a spreadsheet or a system directly. The part that decides whether it works in practice is validation and review: which fields are checked automatically, and how quickly a person can confirm the ones that are not.
What is intelligent document processing?
Intelligent document processing (IDP) is the category of software that classifies documents, extracts structured data from them and validates it, usually with a human review step. Older IDP relied on templates per layout; newer systems use language and vision models that handle unfamiliar layouts, with validation rules to catch their mistakes.
How accurate is AI document extraction?
It depends on the documents, so measure it on yours. A few hundred real documents with checked values give you field-level accuracy before anything goes live. Clean digital invoices are extracted very reliably; faded scans and handwriting less so, which is why uncertain fields are routed to review rather than trusted.
Can the documents stay inside the EU?
Yes. The major model providers offer EU processing options, and self-hosted OCR and open-weight models can keep documents entirely on your own infrastructure. For health data under GDPR Article 9, or client files at a law firm, that is often the deciding factor in the design.
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