Invoice and document automation for finance teams still typing what a PDF already says.
We take the documents that arrive in every format, extract what matters, and route the exceptions to a person with the evidence attached.
A discrepancy stops it. And it waits, often without anything telling the person who could resolve it.
The document arrives. Everything after that is manual.
Finance processes break in the same place regardless of size: a document arrives in a format nobody controls, and a person becomes the interface between it and every system that needs the data.
It arrives in whatever form the sender chose
PDF, scan, email body, spreadsheet or photograph. The variety is the problem, not the volume.
Someone reads it and types it in
Supplier, amount, dates, line items, tax. Twice if two systems need it.
It is matched against something
A purchase order, a contract, a delivery record. Usually by eye.
A discrepancy stops it
And it waits, often without anything telling the person who could resolve it.
It goes for approval
Where it waits again, and the record of why is in someone's inbox.
Somebody reconciles the total later
Because the two systems disagree and nobody knows which is right.
Three failure points, in the order they cost you.
Most tools sold into this space address extraction, which is the cheapest of the three to fix and the least expensive when it fails.
Extraction
The document is read by a person, or by a tool that was tuned for one supplier's layout
Time per document, plus an error rate that surfaces weeks later in a reconciliation
Matching
The document is compared to a purchase order or contract manually
The largest hidden cost. Mismatches are found late, when correcting them is hardest
Exception handling
Anything unusual stops and waits for someone to notice
Elapsed time, missed early-payment terms, and supplier relationships handled by apology
What the process looks like afterwards.
The target is not zero human involvement. It is that a person only sees what needs judgement, with everything they need to exercise it.
Documents captured wherever they arrive
Inbox, portal, upload or scan, with the original retained against the extracted record.
Extraction with a confidence level
Each field carries how certain the system is, which is what makes selective review possible.
Automatic matching where it is clean
Documents that reconcile against an order or contract pass through without a person.
Exceptions routed with evidence
The person sees the document, the extracted values, the record it failed to match and why.
Approvals chased, not hoped for
The system knows what is waiting and on whom, and says so.
A trail for every field
What was extracted, what a person changed, and when. Which is what an audit actually asks for.
What changes, and what we will not promise.
We have no published finance automation case study, so we will describe the shape of the change rather than attach a percentage to your process.
Straight-through rate becomes the number that matters
The share of documents needing no human touch. It is the measure most worth tracking and the one rarely reported.
Review time concentrates
People stop touching every document and start touching the difficult ones, which is a different job.
Errors move earlier
Caught at extraction with a confidence flag rather than at reconciliation weeks later.
Early-payment terms become reachable
Because the delay was usually the approval chase rather than the finance team.
Where a business case needs a figure, measuring your current straight-through rate is the honest starting point. That is what the workflow assessment establishes.
See back-office automationHow the work runs.
Start with one document type
Supplier invoices, or contracts, or claims. Not all of them, because the layouts and the rules differ.
Establish the current straight-through rate
From real documents, so the change is measurable rather than asserted.
Build extraction against your actual documents
Including the supplier whose format nobody likes, because that is where accuracy claims fail.
Set the confidence threshold with finance
Where the system stops and asks is a business decision about risk, not a technical setting.
Connect the matching, then the approval routing
In that order, because matching is where the cost sits.
Widen to the next document type
On the same foundation, with your team able to add rules where possible.
Accuracy, and what the number actually means.
Extraction accuracy is the most quoted and least useful figure in this category. Three questions make it meaningful.
Accurate on which documents
A rate measured on clean, typed, single-supplier documents tells you nothing about a scanned fax from your worst supplier.
Accurate on which fields
Getting the supplier name right and the tax amount wrong is not a partial success in finance.
Measured against what
A rate is only meaningful against a set of documents with agreed correct values, which is the same discipline as any evaluation set.
We build that document set with you before quoting any accuracy expectation, and we do not publish a headline rate because a rate without those three answers is not information.
Financial data, audit and control.
We do not claim a certification we have not been audited against. These four are agreed in writing before build.
The original is always retained
Linked to every extracted record, because an audit asks to see the document rather than the data.
Every change is attributed
What the system extracted and what a person corrected, with who and when.
Segregation of duties respected
The person reviewing an exception and the person approving payment are treated as different roles by design.
Retention decided explicitly
Documents, extracted data and audit records each get a decision rather than defaulting to keeping everything.
The closest published evidence we have.
No published Viithiisys case study is a finance or document automation engagement. The nearest evidence is production AI work where correctness was measured rather than assumed.
Fitelo
Consumer health. A production system running with evaluation and cost controls, which is the same discipline the accuracy section above describes.
Read the case studyConscious Chemist
Beauty and personal care. Answers generated from the brand's own data, with response time measured.
Read the case study
Neither is a finance engagement. They are shown as the closest available evidence that we ship measured systems, not as equivalent work. See all case studies.
Where this stops being worth doing.
The last one is a prerequisite rather than a disqualifier, and building that set is usually the first piece of work.
Low document volume
Under a few hundred documents a month, the build and its maintenance rarely repay themselves.
One supplier, one format
If everything arrives the same way, a template and a rule may be enough without a system.
The upstream process is the problem
If documents arrive wrong because of how they are requested, fixing the request is cheaper.
No agreed correct values
Without a set of documents with known correct answers, accuracy cannot be established and neither can improvement.
Who we need from your side.
Whoever processes the documents today
They know which suppliers cause problems and which exceptions recur, and no system record shows that.
Someone who can set the confidence threshold
Where the system stops and asks is a risk decision that finance owns.
A finance controller or auditor
Involved early, because control questions raised late change the design rather than approve it.
Access to the matching records
Purchase orders, contracts or delivery data, since matching is where the cost is.
What teams ask before they start.
Pick a topic, or ask us directly. We answer every inbound within one business day.
Still have questions?
Talk to a senior engineer, not a bot.
Send us the invoice your team least likes receiving.
The difficult document is the honest test. Describe one type and where it stops, and we will tell you whether extraction, matching or the approval chase costs you most.