Our documents are scans, and some are bad ones. Does that work?

Mostly yes, and the modern models are far better at degraded scans than previous OCR generations. Quality still degrades on genuinely poor input, which is why every field carries a confidence score and low-confidence records go to review rather than into your ledger.

Do we need to tell it about every layout?

No. Unlike template-based OCR, this generalises across layouts, which is what makes it viable when you receive invoices from four hundred suppliers in four hundred formats. Unusual layouts may need examples added to the evaluation set.

How do you stop a wrong number reaching the accounts?

Validation rules plus thresholds. Totals must reconcile, dates must be plausible, supplier and PO must exist, tax must compute correctly. Anything failing validation or below the confidence threshold goes to a human queue with the source page shown alongside.

What is the realistic saving?

For a team spending two full-time equivalents on data entry, typically 70-85% of that time, with the remainder going to genuine exception handling. We model it against your actual volumes in the readiness audit rather than quoting a benchmark.

By use case

Document Processing & Extraction

Turn unstructured paperwork into validated records

Scope this for us Model the savings

Usually owned by

Finance
Operations
Compliance
Claims

85-95%
Documents processed without human touch: > 99%
Field-level accuracy on posted records: 4 hrs
Invoice receipt to posted, from 3 days

The change

What actually differs afterwards

Not a maturity model. The concrete difference in how the work happens.

Today

Two people spend most of the week typing from PDFs
Error rates discovered downstream in reconciliation
Backlog grows whenever volume spikes or someone is on leave
No consistent audit trail for what was read from what document
Suppliers chase payments delayed by data entry

Afterwards

Documents parsed, validated and posted automatically
Confidence scores route only genuine exceptions to a person
Volume spikes absorbed without a backlog
Every extracted field traceable to its source page
Cycle time in hours instead of days

The most reliable automation there is

Document processing is where AI automation most consistently pays back, for three reasons: the input is high volume, the correct answer is verifiable, and the current process is expensive and universally disliked.

It is also where accuracy claims deserve scrutiny. A system that is 95% accurate on invoice totals is not a system you post to a ledger unattended — it is a system that needs the other 5% routed to a person, which is exactly how we build it.

The pipeline

Classify. What kind of document is this, and does it belong in this process at all.

Extract. Fields as typed values with a confidence score and a reference back to the page region they came from. That reference is what makes review fast and audit possible.

Validate. Business rules, not just format checks. Does the total match the line items. Does the supplier exist. Does the PO have remaining budget. Is the tax calculation right for that jurisdiction.

Route. Clean records post automatically. Anything below threshold or failing validation goes to a review queue with the document and the flagged field side by side.

Learn. Corrections made in review feed the evaluation set, so accuracy is tracked over time rather than assumed to hold.

FAQ

Questions we get asked

Let's find out what is actually automatable

Bring a process that annoys you. In 30 minutes we will tell you whether AI helps, what it would cost, and where it would fail — even if the answer is don't bother.

Book a consultation Get a quote

Or email [email protected] · we reply within 1 business day