An evaluation built on evidence, not a summary number
Ask anyone who has evaluated a document extraction tool what actually convinced them, and the honest answer is rarely a published accuracy percentage. It is almost always a moment where they put a real document next to the tool's output and checked, line by line, whether the numbers actually matched — and that moment is exactly what side-by-side comparison is built to make easy.
This page describes how a source document and its extraction sit next to each other, field by field, with every value traceable back to the exact place it came from — the same underlying traceability described more broadly in validation engine, applied here specifically to the evaluation moment where a team decides whether a tool is actually good enough.
That distinction — between trusting a number and checking a field — sounds obvious once stated, but it is exactly what a rushed evaluation skips: a demo document run once, a headline accuracy figure taken at face value, and a decision made without anyone actually comparing a result to the document it came from.
Why a single accuracy figure hides more than it shows
The problem is not that accuracy figures are dishonest — most are measured on a real test set. The problem is that the test set is someone else's documents, in someone else's mix of formats, and a figure computed over that mix says very little about how a tool performs on the specific bank statements or invoices a given team actually processes.
A 98% field-level accuracy figure sounds reassuring until you realize it is an average — which means it is entirely consistent with a tool that is excellent on clean digital PDFs and mediocre on the scanned, multi-column statements that make up a meaningful share of any real document mix. Averaging hides exactly the failure mode that matters most for a migration decision.
What sits on each side
| Side | What it shows |
|---|---|
| Source document | The original PDF, scan or photo, rendered as submitted |
| Extracted data | Every field FlowParse read, laid out as rows and columns |
| Confidence per field | How certain the extraction is about each individual value |
| Source reference | A link from each extracted value back to its exact position on the document |
Four elements, shown together rather than scattered across separate screens — a reviewer checking one figure sees the source, the extracted value, the confidence and the exact location, all without switching context.
This same layout works whether the reviewer is checking one field they are suspicious of, or working systematically through an entire document to build confidence in the tool before committing to it.
How the comparison view is built
Every field the extraction returns is read from the document first, then rendered next to a live view of the source — not a static screenshot, but the actual document a reviewer can scroll and zoom into while the corresponding extracted row highlights alongside it.
Clicking any extracted value jumps the source view to the exact region it was read from, and clicking a region on the source highlights the field it produced. The link runs both directions, because a reviewer sometimes starts from a number that looks wrong and sometimes starts from a part of the document they want to verify was captured at all.
Confidence, shown at the field level
Not every field is read with the same certainty, and averaging that away would defeat the entire point of a comparison built for evaluation. Each field carries its own confidence level, visible directly in the comparison view rather than buried in a separate report a reviewer has to cross-reference.
A field with high confidence and a field with low confidence look different at a glance, which means a reviewer's attention naturally goes to the handful of values actually worth double-checking, instead of re-verifying every field with equal effort regardless of how certain the extraction already was.
When the two sides disagree
An evaluation is only useful if disagreements surface rather than get smoothed over. When a reviewer checks an extracted value against the source and it does not match — a transposed digit, a misread date, a line item attributed to the wrong column — that mismatch is exactly the kind of finding an evaluation exists to produce.
Mismatches found this way are not treated as failures to hide; they are marked and kept as part of the evaluation record, so a team comparing FlowParse against a legacy tool has an actual count of real discrepancies on real documents, rather than an impression formed from a handful of documents that happened to look fine.
How it works
Upload a real document
One you already process, not a demo file — ideally one that has caused problems elsewhere.
Review the side-by-side view
Source and extracted data together, with confidence shown per field.
Check the fields that matter most
Jump straight to low-confidence values, or verify systematically.
Export the result
Excel, CSV or JSON, with confidence and source reference kept as columns.
A comparison, in practice
A finance team evaluating a switch, one representative bank statement, 34 extracted fields.
| Result | Fields |
|---|---|
| Matched source, high confidence | 31 |
| Matched source after a closer look | 2 |
| Genuine mismatch, flagged | 1 |
34 fields, one genuine mismatch — a transaction description split across a line break that had been merged incorrectly — found in minutes because the comparison view made the exact source region visible next to the extracted row, rather than requiring the reviewer to hunt for it across a forty-page statement.
Comparing a batch, not just one document
One document tells you whether a tool can work. A real evaluation needs to know whether it works consistently, which means running the comparison across a batch that reflects the actual mix of documents a team handles — clean digital statements, older scans, invoices from several different suppliers.
Up to 100 documents can be processed and reviewed in the same comparison session, with the same field-level confidence and source traceability on every one — the difference between a single anecdote and a dataset a team can actually make a migration decision from.
Comparing against a legacy tool's own output
Most evaluations are not just "is FlowParse accurate" — they are "is FlowParse more accurate than what we already use." The comparison view does not connect directly to a legacy tool's API, but it produces exactly the artifact needed to make that comparison fair: the same document, extracted with full field-level confidence and source traceability, ready to sit next to the legacy tool's own export for the identical document.
Run the same batch through both tools, export both results, and the difference stops being a matter of impression — it becomes a specific, countable list of fields where one tool matched the source and the other did not, which is the evidence an evaluation actually needs before a migration decision gets made.
What accuracy actually looks like here
FlowParse's own extraction runs at roughly 99% field-level accuracy on standard financial documents. The point of side-by-side comparison is not to repeat that number — it is to let a team verify it against their own documents rather than take it on trust, which is a fundamentally different and more useful kind of confidence.
A team that runs this comparison against a genuinely difficult batch — old scans, unusual layouts, documents that have caused problems with other tools — and finds the mismatch rate low is in a much stronger position to trust the tool going forward than a team that simply read a vendor's published number.
What you get back
Once a comparison is complete, the result exports as Excel, CSV or JSON, with the extracted value, its confidence level and a source reference kept as distinct columns for every field — a record that documents not just what was extracted, but how certain the extraction was and where each value came from.
This is useful beyond the initial evaluation. Many teams keep the exported comparison as part of their vendor-selection documentation, a concrete artifact to point to when someone later asks why a particular tool was chosen over another.
Who this is for
Finance teams evaluating a switch
Building an evidence-based case for or against a migration before committing budget.
Operations leads running a vendor comparison
Comparing extraction tools on the same real documents rather than a vendor demo.
Accounting firms choosing tooling for clients
Verifying a tool's accuracy on the specific document types a client actually produces.
Developers evaluating an extraction API
Checking field-level accuracy against a real document set before integrating.
Running the comparison as a recurring check
Side-by-side comparison is most visible during an initial evaluation, but it does not need to stop being useful once a tool is chosen. Some teams keep running it periodically — a quarterly spot check against a handful of recent documents — as a way of confirming that accuracy on their actual document mix has not quietly drifted.
This matters more than it sounds, because a document mix changes over time — a new supplier's invoice format, a bank that redesigns its statement — and a periodic check catches a new layout that performs worse than expected before it becomes a pattern, rather than after.
From one document to a full evaluation set
Comparing a single document is a rapid first check — useful for a quick gut check on a tool, but not enough on its own to base a migration decision on. A full evaluation set, gathered deliberately rather than whatever happened to be on hand, is what actually answers the question a team is trying to answer.
A well-built evaluation set deliberately includes the documents most likely to cause trouble — old scans, unusual bank layouts, invoices with dense line-item tables — rather than only the clean, easy documents that make any tool look good. The comparison view treats every document in that set with the same rigor, so scaling from one document to fifty changes the size of the review, not the depth of it.
Keeping a long review session honest
Reviewing field by field across dozens of documents is where evaluations quietly lose their rigor — by the fortieth field, attention drifts, and a reviewer starts trusting a green confidence indicator rather than actually re-checking the source. Confidence-based sorting exists partly to counter this: it surfaces the fields most worth a human's attention first, so fatigue sets in after the important checks are done rather than before.
A practical habit that helps further is reviewing in short, focused sessions rather than one long pass — twenty minutes checking low-confidence fields across several documents produces a more reliable evaluation than two hours spent checking every field on one document with steadily declining attention.
Sharing a comparison with a colleague
An evaluation rarely stays with one person. A finance lead running a comparison usually needs to bring in someone else — a controller who wants to see the evidence before signing off, a colleague who handles the document type that caused the most trouble historically — and the exported comparison is built to be handed off rather than re-explained from scratch.
Because the export keeps confidence and source reference alongside every value, a second reviewer can pick up exactly where the first left off, checking the same flagged fields rather than starting a fresh pass over the whole document. That continuity matters more than it sounds — a re-review that starts over from zero tends to either duplicate work or, worse, skip past something the first reviewer had already flagged as worth a closer look.
Where this feature stops
Does not connect directly to a legacy tool's API
Produces a comparable extraction with source traceability for your own documents — comparing it against another tool's export is a manual step.
Does not decide which tool is better
Surfaces the evidence — matched fields, mismatches, confidence — the decision itself stays with the team evaluating.
Does not replace a pilot on production volume
A strong result on an evaluation set is a good signal, not a guarantee that behavior holds identically at full production scale.