Reading the words is easy. Knowing what they mean is not.
Our extraction agents find the exact fields you need, check them, flag what they are unsure about, and link every value back to the spot on the page it came from.
Which document is your team still re-keying by hand?
Thirty minutes with the engineer who builds these agents. Bring a sample.
Six ways raw OCR leaves you the work.
Plenty of tools can read text off a page. Very few can tell you which number was the loan amount.
- 01 A perfect transcription that nobody can do anything with
- 02 A figure lifted out of a table, detached from the row it belonged to
- 03 Two dates on the page and no way to tell which one matters
- 04 A confident value that turns out to be from the wrong page
- 05 No way to check a number without re-reading the document
- 06 Clean output landing in an export that nobody opens
What goes into XtractSol extraction
Each agent is built around your document types, the fields you care about and how you want the data delivered. Start with one document type or handle many.
The documents you actually get
Scans, PDFs, phone photos and mixed quality files, not just the clean ones that look good in a demo.
Answers, not a page to dig through
The specific fields you need pulled out and delivered ready to use: names, dates, amounts, property details.
Structure carries meaning
Columns, tables and rent rolls read as structure, so a figure comes out attached to the row it belonged to.
Attention where it is needed
Values checked for anything missing or inconsistent, with uncertain ones flagged so your team looks at the handful that matter.
Verify at a glance
Every value links to the exact spot on the page it came from, so checking a number takes a look instead of a re-read.
Lands where your team works
Structured data flows into the tools you already use, rather than sitting in another export nobody opens.
Every value, and the spot on the page it came from
Extraction is only half the job. The half that decides whether anybody trusts it is being able to check a value without reopening the document and reading it again.
So each field carries its position. The borrower and the principal amount were read cleanly from typed text and marked confirmed. The legal description came out of a table, with its row intact rather than as a loose figure.
The maturity date is the interesting one. It was handwritten, the agent is not confident, and it says so. Your team checks one field instead of proofreading fourteen.
The agent does the reading and the keying. Your team confirms.
You decide where the review points sit, and the agent flags rather than guesses.
Read
Text captured from the document, whatever condition it arrives in.
AgentLocate
Which text is which field, using the layout as well as the words.
AgentCheck
Values tested for anything missing, malformed or inconsistent.
AgentFlag
Low confidence values marked, and unreadable ones left empty rather than guessed.
AgentConfirm
Your team clears the flags, with the source passage one look away.
Your teamDeliver
Structured data written into your systems, each value still tied to its source.
AgentNinety-nine percent sounds like a promise. It is closer to a warning
Every extraction vendor quotes a headline accuracy figure, and almost all of them are quoting characters. Run the arithmetic on what that means for a field. At 99% per character, a ten character value comes out fully correct about 90% of the time. On a document with fourteen fields, that is not a rounding error, it is a handful of wrong values per file, arriving with the same confidence as the right ones.
Your documents also vary far more than any benchmark. Clean typed text is close to solved. Handwriting and degraded scans are considerably harder, and complex tables sit in between. So instead of a number we cannot honestly stand behind, we build for verification: every value linked to its position on the page, low confidence values flagged for a person, and unreadable ones left blank rather than filled with a guess. Then we measure on your actual documents during the audit and tell you what we find.
A blank field gets noticed. A plausible wrong number gets used, and nobody goes looking for it until it has been in three other systems for a month.
What goes in, and where it lands
We tune on your real formats, then write the results into the systems your team already works in.
Accuracy varies considerably by document type and condition. Handwritten and heavily degraded material is harder than clean text, and we tell you plainly what is workable during the audit.
You see the accuracy before you rely on it
We learn what your documents look like and what you need out of them, then build in stages you can inspect.
Discovery call
The documents you handle and the data you keep re-keying by hand.
Workflow audit
Real samples reviewed, fields defined, and an honest read on what your document types allow.
Build and tune
Tuned on your actual formats, including the awkward ones, and measured before anyone depends on it.
Deploy and refine
Connected to your systems with review checkpoints, then tracked as new document types appear.
How is this different from basic OCR?
Basic OCR converts an image into text and leaves you to make sense of it. Our extraction understands the document: it knows which number is the loan amount and which date is the closing date, checks the values, flags what it is unsure about, and delivers structured data ready for your systems. You get usable fields rather than a wall of raw text.
How accurate is it really?
We will not quote you a single number, because the honest answer depends on your documents. Clean typed text is close to solved and most engines read it above 99% of characters correctly. Handwriting and degraded scans are much harder, and complex tables sit somewhere in between. More importantly, character accuracy is not field accuracy: a ten character field read at 99% per character is fully correct only about 90% of the time. That gap is why we link every value to its source and flag low confidence rather than quoting a headline figure.
Can it handle poor-quality scans or handwriting?
It handles the imperfect scans and phone photos most teams deal with every day. Handwriting and very low quality files are genuinely harder, so the agent flags what it is unsure about for a person to confirm rather than guessing silently. We set realistic expectations for your specific document types during the audit.
What does it do when it cannot read something?
It leaves the field empty and flags it, rather than filling in its best guess. A blank you can see is safe. A plausible wrong value that flows into your system is not, because nobody goes looking for it.
How many documents do you need to tune it?
Enough to cover the variation rather than the volume. A few dozen real examples per document type, including the awkward ones, usually tells us more than thousands of clean copies of the same layout. We confirm what we need during the workflow audit.
What does it cost?
A build fee for the first document type plus a rate per page or per document that scales with volume. You get a firm number after the workflow audit, before you commit to anything.
Ready to get started?
Book a free discovery call and we will map how agentic AI can fit your workflows.