AI Document Processing Agent
An AI agent that reads incoming invoices, contracts, forms, CVs and delivery notes, checks the data, sends doubtful ones to a person and posts the rest into your accounting system or sheet.
AI document processing that extracts, checks and posts your data
AI document processing means software reads each incoming document, decides what type it is, pulls out the fields you care about, checks them against rules, and writes the clean result to your accounting package, ERP or spreadsheet. We build it with large language models that return data in a fixed schema, plus validation and a human exception queue. Documents the system is unsure about go to a named person instead of into your books. It suits teams that retype invoices, delivery notes, forms or CVs every day, and it works best when the documents are varied enough that fixed templates keep breaking.
Everything you need, nothing you don't.
A document inventory with you: every type that arrives, where it arrives from, which fields matter, which can be left blank and which systems need the result, written down before any model is chosen.
Intake connectors for shared mailboxes, upload forms, cloud folders and scanner output, with duplicate detection so the same invoice arriving twice is caught before it is processed.
Classification that sorts each file by type, with an explicit unknown class for anything it does not recognise, rather than forcing odd documents into the nearest category.
Extraction into a strict schema, with the source page and position kept for every field so a reviewer can see where a value came from.
Validation rules specific to you: totals that must add up, tax numbers that must match a format, purchase orders that must exist, dates that must be sensible, suppliers that must be on your list.
Confidence thresholds and an exception queue: a review screen where staff correct or approve flagged documents, with the correction fed back as a record of where the system struggles.
Write-back to Xero, QuickBooks, Sage, an ERP, a Google or Excel sheet, or a custom database, with an idempotent step so a retry never posts the same document twice.
An audit trail that stores the original file, each extracted value, each rule result, the reviewer and the time, with retention periods you set.
Our Process
We begin by collecting forty or so real documents of each type, including the worst ones, and agreeing the fields and rules. We then build the pipeline and run it against that set, recording field-by-field where it is right, wrong or unsure. Those results set the confidence thresholds, so the review queue is neither empty nor flooded. We launch in shadow mode first, where the agent processes documents in parallel with your staff and nothing is posted automatically. Automatic posting is switched on per document type and per supplier only when the comparison supports it. Scope is agreed on a free call.
Technologies We Work With
Extraction runs on Claude or OpenAI models that read PDFs and images directly, with a dedicated OCR service such as a cloud document-AI API added where scans are poor. Schema checks use JSON Schema or Pydantic-style validation. n8n, Make or Laravel queues move files and data. Accounting and ERP connections use Xero, QuickBooks, Sage or custom APIs, and files sit in your own cloud storage or database.
Which documents is this good at, and what do we check on each?
The same pipeline handles many document types, but the checks that make it safe are different for each. This table is a starting point for the inventory we do with you. The fields and rules shown are illustrative.
| Document | Typical fields | Checks that catch mistakes | Usual destination | Risk if wrong |
|---|---|---|---|---|
| Supplier invoice | Supplier, number, date, lines, tax, total | Lines add to total, supplier on list, duplicate number, matching purchase order | Accounting package or ERP | Wrong payment or tax figure |
| Delivery note | Order reference, items, quantities, date | Order exists, quantities within tolerance | Stock or receiving sheet | Stock counts drift |
| Contract | Parties, term, renewal, notice period, key clauses | Dates parse, parties match your records, checklist clauses present | Contract register | A missed renewal or an unnoticed clause |
| Application or claim form | Identity fields, selections, signatures | Required fields filled, formats valid, signature detected | Case system or database | Delay, or a wrong decision on a case |
| CV | Roles, dates, skills, contact details | Consistent dates, personal-data minimisation | Applicant tracker | Unfair screening, which is why a person decides |
How does one document move through the pipeline?
1. It arrives and is fingerprinted
A file lands from an inbox, a folder or an upload form. Before any model sees it, the system computes a fingerprint and checks for a copy. Duplicates are among the commonest causes of double payments, and they are cheap to catch at this point.
2. It is classified
The model labels the file as one of your known types or as unknown. Unknown is a legitimate answer, and those files go straight to a person.
3. Fields are extracted into a schema
The model must return values in the exact structure you defined, and a schema check rejects anything malformed. A field the document does not contain comes back empty, not guessed. For every value we keep the page and location it came from.
4. Rules are applied
Your business rules run as ordinary code, not as model opinion: sums, formats, lookups against supplier and order lists, date sanity. Code is deterministic, which is why we use it for anything that can be checked mechanically.
5. It is routed
If every rule passes and every field clears its confidence threshold, the document continues. Otherwise it enters the exception queue with the reason attached.
6. It is written back and logged
The clean record is posted to your system and the full history is stored. If a post fails and retries, the step is idempotent, meaning the same document cannot be posted twice.
How wrong can it be, and how do we deal with that?
Both OCR and language models make mistakes. They misread a smudged digit, swap a comma and a decimal point, merge two columns in a table, or skip a line on a crumpled scan. We do not publish an accuracy percentage because any figure we quoted would belong to someone else's documents, not yours.
What we do instead is measure on your sample and design for the errors that remain:
- Field-level results. Totals and tax numbers deserve strict thresholds, while a free-text description can tolerate more.
- Redundant checks. If the lines do not sum to the total, one of them is wrong, and the document is flagged without anyone needing to know which.
- Thresholds from evidence. We set them from your test results, then review them against real review-queue outcomes after launch.
- Shadow mode. The agent runs alongside your staff before it is allowed to post anything.
Our article on testing and evaluating AI agents goes into the testing method, and the piece on human-in-the-loop approvals explains the exception queue idea.
What does the exception queue look like in practice?
It is a simple screen, not a spreadsheet of errors. Each item shows the original document beside the extracted values, highlights the fields that failed a rule, states the reason in a sentence, and offers three actions: correct and approve, reject, or send back for a re-read. Every action is recorded with the person's name and time. The corrections are valuable in themselves, because they show which suppliers, layouts or fields cause trouble and where the instructions need improving.
One more practical point: layouts drift. A supplier changes its invoice template, a branch starts using a different form, and yesterday's reliable field becomes today's source of flags. Because the exception queue records every correction, those shifts show up as a rise in flagged items for one sender, and we can adjust the instructions or the rule for that sender rather than discover the problem at month end.
Audit trail, retention and data residency
Finance and legal teams ask the same questions, and we answer them in writing at the start.
- Traceability. For any posted figure you can open the source file, the extracted value, the rules that passed and the person who approved any exception.
- Retention. You set how long originals and logs are kept, in line with your own records policy.
- Where data goes. Extraction usually sends document content to a model provider's API. Providers publish terms on retention, training use and processing region, and these differ by plan, so check each vendor's current documents before you decide. For stricter needs there are options such as regional processing or models hosted in your own cloud account.
- Personal data. Invoices, CVs and forms often contain personal data. A data-protection impact assessment may be sensible. This is general information, not legal advice, so confirm your obligations with a lawyer or data-protection adviser.
Custom agent or an off-the-shelf invoice tool?
If you only process supplier invoices and your accounting package has a built-in capture feature, try that first; it is the cheapest route. A custom build makes sense when you handle several document types, when your validation rules are particular to you, when documents must be matched against internal records, or when you need review screens and logs shaped to your process. Our invoice automation for small business page shows a smaller, invoice-only setup, and the n8n workflow service covers the automation layer. For heavier internal systems, see our web application development work.
When you should not buy this
- You process a few dozen documents a month. Typing them is cheaper.
- Your documents are all the same template, in which case classic template-based capture is simpler and more predictable.
- Nobody has time to review exceptions. An agent with no reviewer is a risk, not a saving.
- You want a model to make hiring, credit or similar decisions on its own. We would not build that.
If you are unsure which category you are in, send us a few sample documents and a description of the current process and we will give you an honest view.
Client feedback
What clients say
“Usama made an old first time app builder very confident with his professional approach, attention to details and a complete understanding of what I was trying to convey. Usama exceeded my expectations on our first meet. Would highly recommend Usama and his team.”
“Great experience working with Usama. He delivered a well-structured, conversion-focused product page and was very responsive to feedback. The final result is much more professional and aligned with improving sales performance. Communication was smooth, and he implemented all requested adjustments quickly. Would definitely recommend and work with him again.”
“Usama went above and beyond to help me, especially as I changed directions quite a few times, however his professionalism throughout made the whole experience, for an inexperienced person, very easy and informative and enjoyable. I would highly recommend Usama and his team.”
“Great job as always.”
“Very nice working with Usama. Will continue using his services.”
“It was a pleasure working with Usama. I am very satisfied with the communication, the final results and the expertise in this field. Looking forward to future collaborations.”
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How pricing works
We quote every project individually, because the price depends on scope: how many pages, systems or integrations are involved, how much is built from scratch, and how fast you need it. Small, well-defined jobs cost far less than custom builds. Tell us what you need, or use the quote request guide, and we will reply with a written quotation.
Frequently Asked Questions
Can AI extract data from invoices accurately?
It can extract most fields from most invoices, but not all, and no honest vendor can promise a single accuracy figure for your documents. Error rates depend on scan quality, layout variety and language. We test on your own invoices, set thresholds from the results and route anything doubtful to a person before it reaches your accounts.
What is the difference between OCR and an LLM for documents?
OCR turns the picture of a page into text. An LLM can then work out which text is the invoice number, the total or the supplier, even when layouts vary. Modern models can read images directly, but poor scans still produce mistakes, so we often combine both and check the output with rules.
What happens when the AI is not sure about a document?
It goes to an exception queue instead of being posted. A reviewer sees the document, the extracted values and the reason it was flagged, such as a total that does not match the line items. They correct or approve it, and the decision is logged. Nothing posts to your accounting system without passing the rules or a person.
Can it process contracts, CVs and delivery notes too?
Yes, with different rules for each type. Contracts need clause and date extraction, ideally with a lawyer-set checklist. CVs raise fairness and privacy questions that need careful handling, and automated hiring decisions can be regulated, so a person must decide. Delivery notes are usually the easiest, because the key check is matching them to a purchase order.
Where is my document data stored and sent?
Files can stay in your own storage, but extraction normally sends content to a model provider's API. Providers publish terms on retention, training use and processing location, which differ by plan and region. We help you pick a setup that matches your policy, and you should check each vendor's current terms. This is not legal advice.
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