Quick summary
Automating the processing of invoices from e-mail and Outlook removes routine retyping of details and the hunting for errors. An invoice arrives, the system extracts, checks, and prepares it for payment on its own — and you deal only with the exceptions that require human judgement.
- Extraction without retyping — OCR and AI read PDF invoices and scanned images and pull out the company ID, VAT number, amounts, due date, and variable symbol.
- Checking the required items before posting — the system compares the supplier with the VAT-payer register, verifies the account-number format, and flags discrepancies before the invoice reaches accounting.
- Matching payments from the statement — an incoming payment is automatically matched against open liabilities, and manual searching falls away.
- A human in the loop for risky steps — you step into the flow only at the point of uncertainty: an unknown supplier, a suspicious duplicate, or an amount outside tolerance.
How invoice automation works
The entry point remains the e-mail inbox or Microsoft Outlook, where suppliers commonly send their documents. The system watches defined folders, pulls out attachments in PDF, JPG, or PNG format, and passes them on for extraction. OCR extracts structured data — the invoice number, issue date, due date, unit prices, and VAT rates. The result is not just raw text, but a tagged data record ready for validation.
The validation layer uses both AI and fixed rules. It verifies the existence of the supplier in the master data, recalculates a checksum, and checks for duplicates across already-processed documents. Only once an invoice passes all the checks is it automatically recorded in the accounting system — whether via API or a structured import. The human-in-the-loop principle means that only flawless and predictable documents pass through fully automatically. Everything else, from an unknown supplier to a suspiciously high amount, is stopped and waits for conscious approval. The follow-up step of matching against the bank statement then runs again without human intervention.
Thanks to this chain, an hour of searching becomes a few clicks of work. The accounts department stops being a retyping centre and becomes a control point where only the substance is handled. The whole concept fits into the broader process automation we promote in companies across departments.
What all can be automated
The scope of automation is determined not just by technology, but mainly by the nature of your documents and processes. In practice we almost always start with extraction, then add a checking and approval layer.
- Extracting PDF and scanned invoices — machine-readable and image data are processed with the same reliability, including multilingual documents.
- Duplicate checking — the system compares key parameters against the history and prevents double payment, even if a supplier sends a corrected invoice from a different address.
- Matching payments against the bank statement — once a liability is posted, incoming payments are automatically assigned by variable symbol, amount, and reference.
- Approval workflow — invoices above a set limit go to approvers according to the organisational structure, including escalations in case of inactivity.
- Watching due dates and notifications — an approaching due date triggers an alert, so the company does not lose an early-payment discount or pay needless penalties.
When it pays off
Automation makes sense at the point when the volume of repeated documents grows and with it the overhead of manual processing. Typically this means an operation with a stable set of suppliers, similar items, and a uniform approval procedure. The more stable the environment, the higher the degree of automation that can be deployed without a rising number of exceptions.
But there are situations where automation does not make sense — and we say so straight. If only a few documents arrive, each with a different structure, the project does not pay off. The same applies if the source documents cannot be standardised or the process requires complex human interpretation at every step. In such cases it pays to start first by adjusting the processes and only then consider technology. Correctly set up, Business process automation with AI respects the boundary beyond which human judgement is irreplaceable.
How we deploy it
- Audit and analysis of the documents — we go through your real invoices, identify recurring patterns and bottlenecks, and propose the scope of automation, including safe fallbacks for non-standard cases.
- A pilot on real invoices — on a limited sample of documents we verify the accuracy of extraction and the correctness of the connection to the accounting system, fine-tune the validation rules, and involve the responsible people.
- Deployment and operation with support — after sign-off we move into routine mode. We oversee operation, update the rules according to changes in legislation, and remain available for process adjustments.
Why ITHOPE
- One partner for both IT and automation — you do not deal separately with an OCR tool, the workflow, and the accounting system. We deliver the whole thing, from infrastructure to process logic.
- Connection to accounting systems via API and export-import — we integrate with common ERPs without having to change your existing software.
- Local operation for sensitive data — invoices and bank statements stay under your control, and sensitive data does not have to leave your infrastructure.
- We build and operate over the long term — we not only design the automation but also ensure its operation, tuning, and adaptation to changes in your processes.
- We do not automate at any cost — where automation does not make sense, we recommend a process change or keeping human control. We build solutions that pay off.
Send us a sample of your invoices
Do you want to know whether automation will pay off for you specifically? Send us a few anonymised samples of your invoices — with no obligation we will assess their structure and tell you what degree of automation can realistically be achieved. On the sample data we will show what the system can extract and where a person would have to step in.