Automation that reads messy documents and asks when unsure
Classic robots are good at rule-based work with tidy inputs. Some work starts with untidy inputs instead: a scanned delivery note, a foreign invoice as a PDF, a free-text customer e-mail. We use AI to read and classify these inputs, then connect the result to your workflow under the same controls our robots follow.
Where it helps
AI is useful where the work needs interpretation rather than a fixed rule. Typical work we can automate:
- Reading fields from scanned or PDF invoices, delivery notes, orders and receipts
- Sorting messages in a shared mailbox by topic and routing them to the right person
- Turning free-text requests into a standard form
- Pulling specific facts out of long documents such as contracts or specifications
- Preparing a short summary and suggestion when a robot marks an item “needs approval”
We measure on your documents first
How accurately AI reads a document depends heavily on the document type, scan quality and the number of layouts, so instead of promising a general figure we measure on your own documents.
We run a pilot on a sample of real documents and count, field by field, how many were read correctly. You decide from that table which fields can pass automatically and which go to a person for approval.
One step inside a larger workflow
We rarely use AI on its own. More often it is one step inside an existing automation: a robot that posts invoices to SAP reads structured e-invoices directly, while PDFs and scans go through an AI reading step first and then join the same flow under the same checks. Structured electronic documents do not need AI at all; using it there would only add a source of error. The result is written wherever it needs to go: SAP, Logo, Netsis, Mikro, Dynamics 365, a CRM or an Excel list.
A safety net: human approval and a record
AI is sometimes wrong. Every field is read with a confidence level, results below the threshold are never written to a system but sent for human approval, and every approval or correction is recorded. Over time you can see which document types need correcting and how often.
Where the data is processed matters too. Most AI services run in the cloud, and if your documents contain personal data or trade secrets, data protection rules apply. We set out the options in writing: models that run on your premises, enterprise services with a data processing agreement, or removing personal data before anything is sent. You decide with your legal and IT teams. Our default is that data stays in the company.
Questions
How accurate is AI invoice reading?
It varies widely with document type, scan quality and the number of layouts, so a general figure would mislead. We recommend a small pilot on your documents, measured field by field.
Does the AI make the decisions?
No. It reads, classifies and suggests. Low-confidence results and any step that creates a record go through human approval, under rules you set.
Can it be added to robots we already have?
Yes, and that is usually the most sensible use. The robot keeps working with tidy data; only the messy inputs go through the AI step.
Give us 30 minutes and one job.
Tell us about a repetitive task or a tool your team is missing. We'll tell you plainly what it needs. If it needs nothing, we'll say that too.
Free, 30 minutes, no commitment.