AI Business Automation
AI document processing that reads invoices, purchase orders and forms and puts the data where it belongs.
What you get
Plenty of business still runs on documents. Customers send purchase orders as PDFs. Suppliers send invoices as scans. Carriers send delivery notes as photos. Somebody has to read each one and type the details into NetSuite, Xero, QuickBooks or a spreadsheet, and that somebody is usually expensive and bored.
Older OCR tools could handle this only when every document looked the same, and they needed a new template for each supplier. AI document processing with modern language and vision models is far more flexible. A model can read an invoice it has never seen before, find the line items, tell the difference between the billing and shipping address, and return clean structured data.
That flexibility comes with a catch: models can be confidently wrong. So we never post raw model output straight into your books. Every extraction is checked against rules (do the line items add up to the total, does the PO number exist, is the supplier known) and anything that fails or looks unusual goes to a person for a quick review.
This is one of the most common starting points for our AI business automation work, especially for finance teams and wholesale distributors. Once the data is extracted, workflow automation takes it through approvals and into the right system.
The problem
Documents arrive in every format imaginable: clean digital PDFs, scanned pages at an angle, Excel files pretending to be invoices, photos taken on a warehouse floor. Each customer and supplier has their own layout, and they change it without telling you.
Manual entry copes with this variety, but slowly and with errors. A transposed digit in a quantity or a missed line item causes problems that surface weeks later in a stock count or a supplier dispute. And at month end, the backlog lands on the same few people at the worst possible time.
What we build
Watch a shared inbox, a Google Drive or SharePoint folder, an SFTP drop or an upload form, and pick up new documents within minutes.
Tell an invoice from a credit note, a PO from a quote, or a delivery note from a packing list, and route each to the right extraction schema.
Pull header fields like dates, totals and references, plus full line-item tables with SKUs, quantities and prices, into structured data.
Check totals, match suppliers and SKUs against your master data, and confirm PO numbers exist before anything is posted.
Uncertain or failed documents appear in a simple screen with the original side by side, so a reviewer can fix a field in seconds.
Create bills, sales orders or records in NetSuite, Xero, QuickBooks, your ERP or a Postgres database once a document passes checks.
Examples
A distributor receives dozens of PO emails a day in different layouts. Each PDF is extracted, customer part numbers are mapped to internal SKUs, and a draft sales order is created. Orders with unknown items or unusual quantities are held for review.
Invoices sent to an accounts inbox are read, matched to the supplier and PO, and created as draft bills in Xero with the PDF attached. Price or quantity mismatches are flagged for the accounts team.
Drivers upload photos of signed delivery notes. The pipeline reads the consignment number, signature presence and any damage notes, updates the shipment record and alerts customer service about exceptions.
A property manager uploads leases. Key terms such as rent, review dates, break clauses and renewal notice periods are extracted into a database, and upcoming dates feed into a reminder workflow.
How we build it
We begin with your real documents, not a demo set. You share a sample (typically 50 to 200 across your common layouts), we agree the fields you need, and we measure extraction accuracy field by field before anything goes live. That gives you an honest picture of what will run untouched and what will need review.
For clean, consistent documents, a dedicated OCR service such as Azure AI Document Intelligence or AWS Textract is fast and cheap. For varied layouts and messy scans, vision-capable models like Claude or GPT read the page more like a person would. We often combine them: OCR for the text, a language model to interpret it.
Model costs are per page, so we track them and route simple documents to cheaper methods. To see how we run a project from sample documents to production, take a look at our process.
It depends heavily on document quality and layout variety, which is why we test on your own documents before go-live. Clean digital PDFs usually extract very well; poor scans and handwriting less so. Validation rules and review queues catch the errors that slip through.
Generally no. Modern models handle new layouts without templates. For a few high-volume suppliers we may add specific rules to improve accuracy further.
Partly. Neat handwriting on forms often works; scrawled notes are unreliable. We'll test your examples and tell you honestly which fields should always go to a person.
In storage you control, such as your own cloud bucket, SharePoint or Google Drive. Documents are sent to AI providers only for processing, through business API tiers that don't train on your data.
Usually a small amount per page in model or OCR fees, plus hosting. The exact figure depends on page count and method, and we estimate it from your volumes during scoping.
Yes. Spreadsheets are often easier than PDFs, but column names and layouts vary. We map them the same way and apply the same validation before posting.
Share a handful of real invoices, POs or forms (redacted if you prefer). We'll tell you what can be extracted reliably and what a pipeline would look like for your volumes.
We pick tools for your situation, not ours. These are the ones we reach for most on this kind of work.
We don't just build for clients. These are our own products, used by real stores. See all products.
A step-by-step method for estimating the ROI of an automation project before you build it and measuring it honestly afterward, with a clearly hypothetical worked example.
How AI document processing turns invoice and purchase order PDFs into validated, structured data in your ERP or accounting system, and the checks that keep it accurate.
A practical scoring framework for choosing your first automation projects, so you start with work that pays back quickly instead of the process that is simply the loudest.
Tell us how it works today. We'll tell you honestly what's worth automating, what it would take, and what it would cost.
