Document extraction and review
Document extraction and review automation
Invoices, receipts, forms, statements and scanned PDFs arrive every day, and someone on your operations, finance or admin team keys or checks the fields by hand. For small and mid-sized US companies, I build AI extraction pipelines that read those fields, score their own confidence, and route only the uncertain documents to a person. In my published benchmark, AI extraction beat a rule-based OCR pipeline by 9.25 percentage points in per-document accuracy across 1,497 documents (997 synthetic invoices from 50 templates plus 500 public receipt scans, ICDAR 2019 SROIE): about 3 points on invoices and about 22 on receipts. The time saving from confidence-based review is modeled, not yet measured with real reviewers, and it is the first thing a pilot measures.
Below the monthly volumes given in the benchmark’s Part II, the AI pipeline costs more per document than the status quo. If your volume is low, you’ll hear that from me before anything is built.
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Sound familiar?
Where hand-checking fields breaks down
Every invoice is keyed or checked by hand
Vendor, date, total, tax, line items: someone reads each one off the page and types it into the system, and the queue grows with the business.
OCR output needs as much correction as it saves
A template-based OCR tool handles the layouts it was configured for. A new vendor format or a crooked scan sends the document back to a person, and correcting the output is the job the tool was meant to remove.
Nobody knows the error rate
Fields get fixed when someone notices. Nobody can say what share of extracted values were wrong last month, because nothing is sampled and scored against ground truth.
Auto-accept is too risky to switch on
Letting documents through without a person looking is where the saving is. Without a measured false-accept rate the risk is unknown, so the review step stays on for everything.
How I approach it
How the pipeline gets built and measured
Sample and ground truth first
Together we identify the document types and the fields that matter. You supply a sample of each type, on the order of 100 to 300 documents, and a reviewer records the correct values for a subset. That subset is the ground truth every later number is scored against.
Run both pipelines on your documents and measure
AI extraction and a rule-based OCR pipeline both process the sample. Per-field and per-document accuracy are reported for each, on your documents, so the decision to proceed rests on your error rates rather than on my benchmark’s.
Set the threshold from the measured false-accept bound
A consensus layer compares extractions from more than one engine and scores confidence per field. The auto-accept threshold is set from the false-accept rate observed on your sample, and the sample size limits what that rate can promise: in my benchmark, zero false accepts on about 62 auto-accepted documents only bounds the rate below roughly 5%. Everything under the threshold goes to a person.
Route, log, then time the reviewers
Documents above the threshold are accepted; everything else lands in a review queue with the uncertain fields highlighted. Every decision is logged. Reviewer time is measured with your real reviewers during the pilot, before any time saving is claimed. The benchmark’s roughly 49% reduction in invoice handling time at the recommended conservative threshold comes from a simulated review workflow, and that is the number a pilot has to confirm or replace.
What you get
What the engagement delivers
- An extraction pipeline on your document types, with a consensus layer and per-field confidence scores.
- A measured accuracy and false-accept report on your sample, for both pipelines.
- A review queue with confidence-based routing: auto-accept above the threshold, everything else to a person.
- Integration into the system the data goes to.
- Handoff documentation, plus the ground-truth set so your team can re-measure after any change.
Limits worth knowing before you ask
The published benchmark is the evidence for the method. Its limits are what a pilot on your documents is designed to close:
- The invoices were synthetic, generated from 50 templates; the receipts were public scans.
- Every review-time figure is modeled from assumed parameters; no reviewer was timed.
- Commercial document-AI services were not compared; the baseline was an open-source OCR pipeline with positional rules.
- Generalization beyond the tested templates and the public receipt set is not shown; your sample is what shows it.
Backed by published testing
The benchmark behind this service
Fit check: this is the extraction step the other service pages refer to. It works best on documents with a stable set of fields: invoices, receipts, forms, statements. Handwriting, low-resolution scans and free-form documents without defined fields are harder, and I’ll say so during discovery.
How an engagement runs
Free consultation and discovery. You describe the workflow; I tell you whether AI is the right tool, and say so if it isn’t.
Scoped assessment or fixed written proposal. Scope, price and success criteria are agreed before any build starts. See what the scoped assessment covers.
Pilot on one workflow, typically 8–10 weeks, measured against a baseline before anything goes hands-off.
Phased rollout, with a stop option after any milestone.
What I’ll need from you
- A sample of each document type, on the order of 100 to 300 documents, with the correct field values for a subset.
- The list of fields that matter and their formats.
- A reviewer who checks fields today.
- An IT contact for the system the data goes to.
- For self-hosting, a GPU server or cloud account sized during discovery.
Questions
Questions about extraction and review
How is this different from an OCR vendor or a commercial document-AI service?
The benchmark compared AI extraction with an open-source OCR pipeline with positional rules; it did not compare commercial document-AI services, so I can’t tell you from the study how they would do on your documents. What I add is the measurement: ground truth from your own sample, accuracy and false-accept rates for whatever pipeline you run, and a threshold set from those numbers. If a commercial service wins on your sample, that is the recommendation.
Is auto-accept safe?
Only within the limit the sample can show. The benchmark observed no false accepts, but with about 62 auto-accepted documents the sample can only show that the rate is below roughly 5%, not that it is near zero. On your documents the threshold starts conservative, the false-accept rate is measured on the pilot sample, and the threshold moves only when the numbers support it. Fields with the highest cost of error, such as totals and bank details, can be excluded from auto-accept entirely.
What does a “modeled” time saving mean?
The benchmark’s handling-time figures come from a simulation: assumed handling times per document, applied to the measured accuracy and the routing decisions. No reviewer was timed. At the recommended conservative threshold the simulation predicts roughly 49% less invoice handling time; the pilot tests that prediction with your reviewers and a stopwatch, and the pilot report shows the measured figure next to the modeled one.
Which document types work?
The types in the fit check above: documents with a stable set of fields. Layout can vary between vendors; what matters is that the fields are defined, so a new vendor format is usually fine and a handwritten note is not.
What happens to a low-confidence document?
It goes to the review queue with the uncertain fields highlighted and the extracted values pre-filled, so the reviewer confirms or corrects instead of re-keying. The decision, the reviewer and the correction are logged. Corrections feed back into the ground-truth set, which is how the accuracy numbers stay current after launch.
Does it run on our infrastructure?
It can. The pipeline deploys on open-weight models on your own server or in your private-cloud account, so documents are not sent to an AI vendor; a managed API is also an option where your policies allow it. The trade-offs in cost, speed and data handling are documented during discovery.
- Insured: professional liability (E&O), cyber and general liability, bound before any engagement starts.
- You own the deliverables built for your environment.
- Stop after any milestone; no lock-in.
- Replies within one business day.
Find out what this would look like for your team
The consultation and initial discovery are free. You get a preliminary recommendation whether or not we work together.
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