Document Processing Consulting

In claims, in AP, in patient access, most of the day goes on reading documents and typing what they say into another system. The deciding part, the bit that actually needs a person, is a small slice at the end. We build the reading part so your team gets the exceptions and not the stack, and we are specific about which documents are safe to automate and which are not.

Document processing that extracts, validates and routes the paperwork your team currently reads by hand, with a confidence threshold and a human on the exceptions.

Document processing, often sold as intelligent document processing, is the automated extraction of structured data from documents such as invoices, claims forms, remittances and identity paperwork, followed by validation against a system of record and routing of anything the model is not confident about to a person.

Document processing extracts data from invoices, claims, remittances and intake forms, validates it against your systems, and routes low-confidence cases to a reviewer. The accuracy that matters is not the model's score on a benchmark but how often it is right on your documents, so Thinklytics measures it on a sample of your real paperwork before scoping a build, and prices that first stage separately.

Extraction measured on your documents, not on a vendor benchmark. The same model reads a clean PDF and a photographed fax very differently.

Validation against the system of record, so an extracted value is checked rather than trusted.

A confidence threshold with a person behind it. Everything below the line goes to a reviewer, and the line is set from the cost of a wrong answer.

A written list of what stays manual, because some documents are rare enough or consequential enough that automating them is the wrong trade.

An accuracy percentage in a slide. 95 of 100 sounds fine until you work out what the other 5 cost and who finds them.

A scanner. Getting text off a page is the easy part; knowing whether the number is right is the work.

A replacement for the team. It removes the reading, which is most of the volume, and leaves the judgement, which is most of the value.

A single model for every document type. An invoice, a loss run and a remittance advice are different problems and get scoped separately.

An accuracy baseline measured on a sample of your real documents, broken down by document type and by field.

The extraction pipeline, wired into the system that already owns the record.

Validation rules that check an extracted value against existing data rather than accepting it.

A confidence threshold, a review queue, and a route for anything the model will not commit to.

Exception handling that shows the reviewer the page and the field, so checking is quick.

The measurement that keeps running after launch: straight-through rate, correction rate, and what the corrections have in common.

Prior authorisation review time, with 18,000 more cases handled a month and no extra staff.

Misrouted claims recovered once member match accuracy went from 75 to 94 of every 100 records.

Unlocked for AI underwriting after claims data from four outdated systems was combined.

The tool reads our documents well in the demo and badly in production.

The demo used clean samples. Real intake carries photographs, faxes, handwriting and forms from a dozen senders, and accuracy has to be measured on that mix.

No confidence threshold, so there is no basis to let anything through. Without one, extraction is just a slower way to read.

Extracted values were trusted rather than validated against the system that already holds the answer.

Straight-through rate and correction rate were never instrumented, so the only signal is whether anybody has complained lately.

It is the automated reading of documents such as invoices, claims forms, remittances and intake paperwork, turning them into structured fields, checking those fields against a system of record, and sending anything uncertain to a person. The extraction is the visible part. The validation and the routing are what decide whether it saves anyone time.

It depends entirely on your documents, which is why a vendor's benchmark number tells you almost nothing. A typed PDF from a single sender behaves very differently from a photographed form filled in by hand. We measure accuracy on a sample of your real paperwork, by document type and by field, before anyone scopes a build, because that is the number the business case rests on.

It goes to a reviewer, and where that line sits is a business decision rather than a technical one. A wrong vendor name on an invoice is a nuisance. A wrong coverage decision on a claim is not. We set the threshold from the cost of being wrong on that document type, and the review queue shows the page alongside the field so checking takes seconds.

Anything rare enough that the model never sees enough examples, and anything consequential enough that a quiet error is unacceptable. Those stay manual on purpose and we say so in writing. A list of what we are not automating is part of the deliverable, because an honest scope is what stops the project unravelling in month four.

No. The extraction writes into whatever already owns the record, your claims platform, your ERP, your practice management system. Replacing the system of record is a far larger project and is almost never what the document problem actually requires.

Measuring accuracy on your real documents takes two to three weeks and often changes the plan. A scoped build on one document type usually runs eight to twelve weeks after that. Several document types are several engagements, not one bigger one, because each has its own accuracy profile and its own exception rules.

The drivers are how many document types are in scope, how varied the senders are, how much validation the business needs, and whether a review workflow has to be built or already exists. Thinklytics prices by deliverable rather than by an hours bucket, and the accuracy baseline is priced separately so you can stop there if the numbers do not justify a build.

OCR turns an image into text. Document processing works out which text is the invoice total, checks it against the purchase order, and decides whether a person needs to look. OCR is a component, and treating the two as the same thing is why a lot of these projects deliver a faster way to retype.

The workflow around the extraction, once the reading is handled.

Measure where the time goes before deciding what to automate.

For when the record the extraction validates against is itself unreliable.

Whether your documents and systems can carry the use case at all.