AI data extraction that writes clean records into your systems

Your team copies the same fields from PDFs, scans and email attachments into your ERP, CRM or spreadsheets, then fixes the typos a month later. We build a workflow that reads each document, checks every field against your rules and writes the record through your system's API, with only the uncertain fields sent to a person.

Book a Discovery Call See what the workflow does

Jason, our COO, runs the call and walks through your documents with you.

Supplier form, extractedSample

New vendor registration, 2 pagesScanned PDF, received in the procurement inbox

Legal nameKestrel Ridge Freight LLC99%
EIN84-221093798%
Remit-to addressTacoma, WA 9842197%
Signed on03/1?/202661%

{ "vendor": "Kestrel Ridge Freight LLC",
  "ein": "84-2210937",
  "state": "WA",
  "signed_on": "2026-03-14" }

10:02Classified as vendor registration, 9 fields extracted
10:02Handwritten date below your 90% threshold, sent to review
10:41Reviewer confirmed 14 March
10:41Vendor record written to the ERP, source PDF linked

Clients include

Automated data extraction in five steps

Each step runs as its own node in the workflow, so you can open any document and see what was read, what was checked and who approved it.

01
Inbox, upload, shared drive or API
Capture
Email attachments, portal uploads, scans and files dropped into a shared folder all land in one queue, each with its source and arrival time recorded.
02
By content, not by file name
Classify
A language model reads the document and decides what it is: a contract, an application, a statement, a delivery note. A document it cannot place goes to a person instead of the wrong extraction rules.
03
Any layout, a confidence score per field
Extract
The model pulls the fields you define for that document type, including tables that run across pages, and scores its confidence in each one. New layouts need no template.
04
Against your rules and your live data
Validate
Dates, amounts and IDs are normalized to one format, required fields are checked, totals are recalculated and values are compared with the records already in your systems. Anything below threshold or out of rule goes to review.
05
Through your system's API
Write and log
Clean records are written to your ERP, CRM, database or warehouse, with the source document linked. Every field, check, correction and write is logged.

Data extraction services for the documents your team still keys in

You define the fields and the rules for each document type. The same five steps run underneath, so a second document type adds its fields and rules to the workflow you already run.

Finance

Invoices, receipts and statements

Header, line items, tax and bank details, matched against purchase orders and posted to your ERP. Invoices have their own page: AI invoice automation.

Legal and procurement

Contracts and vendor forms

Parties, dates, renewal terms, payment terms and signatures, written to your contract register or vendor master, with the clause each value came from linked.

Operations

Orders, delivery notes and certificates

Order numbers, quantities, lot numbers and expiry dates from the paperwork that arrives with every shipment, checked against the order in your system.

Customers

Applications, claims and intake forms

Names, policy or account numbers and the answers on each form, written to your CRM with missing or conflicting answers sent back for follow-up.

Only the uncertain fields reach your team

Documents that pass every check are written without anyone opening them. The rest arrive in Slack, Teams or email with the problem already named, as in this sample.

Review reason	What the workflow found	Sent to	What the reviewer does
Low confidence	Handwritten date read at 61%, below a 90% threshold	Data owner	Confirms or corrects the one field, with the scan beside it
Missing field	Signature page absent from a two-page form	Requester	Asks the sender for the missing page
Rule failure	Line items add up to $4,210, the stated total is $4,120	Finance	Picks the correct figure or rejects the document
Conflicting record	Tax ID matches an existing vendor under a different name	Procurement	Merges, updates or creates a new record
Unknown type	Document fits none of the defined types	Workflow owner	Assigns a type, or adds a new one to the workflow

AI document processing that ends in your system of record

Extraction is only useful once the data is where your team works. The workflow reads from and writes to the systems you already run.

ERP

SAP S/4HANA, Odoo and others

Records go in through the ERP's API, with the same validation your team applies by hand. Our guide to how agents connect to your ERP, CRM and data shows the layers involved.

CRM and helpdesk

HubSpot, Zendesk and others

Form answers and attachments become contact, deal or ticket fields, so nobody retypes an application into the CRM.

Data

Databases and warehouses

Structured JSON or rows in your database or warehouse, ready for reporting, with the source document kept next to every record.

Rules

Your field definitions and thresholds

Document types, fields, formats and thresholds sit in a context layer the agents read before they act, so changing a rule is one edit. Not sure what your systems expose? The AI Audit maps it first.

The same checker pattern, already in production

The validate step is an agent that checks every output against your rules before anything is written. IES Limited runs that pattern on regulated insurance content, across four brands in the UK, Ireland and Australia. See all case studies

IES LimitedInsurance, compliance-checked content pipeline

~~4 brands~~1 pipeline

Four brands and three regions on one system

A checker agent audits every draft against each brand's rules before a person reads it

The agent must answer in structured JSON, correct or improvement needed, with each issue named. In data extraction the same step checks each field against your formats, totals and existing records.

11stages from one trigger

0external agencies in 2026

100+topics generated

Read the IES Limited case

We've completely phased out our reliance on external agencies and freelancers in 2026 while significantly speeding up production.”

Manual data entry compared with an AI extraction workflow

Each row shows what changes for the people who handle your documents today.

	Ovidius extraction workflow	Manual data entry
Reading documents	Any layout, with a confidence score on every field	Read and typed by hand
Checking values	Formats, totals and existing records checked on every document	Spot checks, when there is time
Problems	Named, and sent to the person who can fix them	Found later, in a report or by a customer
Getting data into systems	Through each system's API, every write logged	Copied between screens
New document type	New fields and rules on the same workflow	Another person, or more overtime
Who owns it	You, as documented n8n workflows your team can extend	Whoever knows the process

Questions about AI data extraction

Ask us something else

How long does an AI data extraction build take?

It depends on how many document types, sources and target systems are involved, so we scope it before you commit. One document type written to one system usually takes weeks; several types across several systems take longer, and scoping tells you how much. For a sense of pace, GoKickflip's pipeline was built and shipped in 30 days.

Is intelligent document processing AI?

Yes. Intelligent document processing combines text recognition with language models that understand what a document says, so it can find a total or a renewal date on a layout it has never seen. What we build adds the steps around it: validation against your rules and records, a review queue for uncertain fields and the write into your systems.

How is this different from OCR?

OCR turns an image of a page into text. It does not know which number on the page is the total or whether that total matches the line items. The extraction step reads that text the way a person would, picks out the fields you defined, and the validation step checks them before anything is written.

Which AI model do you use for data extraction?

We test models from approved providers on a sample of your own documents during scoping and pick the one that meets your accuracy and cost targets for each document type. Model calls go only to providers you approve, and the workflow can switch models without being rebuilt.

What happens when the AI is not sure about a field?

Every extracted field carries a confidence score, and you set the threshold per field. Below it, the document goes to review with the uncertain field highlighted next to the scan, and nothing is written on a guess. We log each correction and use it to tune prompts and rules during the first weeks in production.

Can it read scans, photos and handwriting?

Scans and phone photos, yes. Handwriting varies too much to promise in advance, so we measure it on your own samples during scoping and set thresholds from those results. Handwritten fields that fall below threshold go to review, as the date does in the sample above.

Where does our document data go?

The workflows run on n8n, which can be self-hosted inside your own cloud, and model calls go only to providers you approve. Hosting, retention and access rights are agreed in writing before the build starts, after an NDA and a data governance agreement are signed. The controls follow our enterprise AI governance setup.

Can you extract data from documents we already have in storage?

Yes. The same workflow can run over an archive in batches, with the review queue sized so your team is not flooded, before it switches to new documents as they arrive. If documents are one of several processes you want automated, our AI consulting team scopes them together.

Put your first workflow on the board

Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.

Owen, Maciej, Jason, Oskar and Ben