Solution
For operations leads and data teams running high-volume invoice extraction: 100,000 scanned pages a day read without templates, every value deep-linked to the page it came from.
The problem
100,000 scanned invoice pages arrive every day across many vendors and templates — quality-checked by an in-house, on-shore QA operation.
The desktop OCR product went out of maintenance and accuracy started deteriorating, with a fleet of per-template JavaScript parsers to keep alive.
The extractions that matter most are the ones that defeat OCR-plus-rules: dense line-item tables, vendor quirks, degraded scans.
The product, not a promise
How it works
Scanned invoice PDFs stream in — multiple vendors, multiple templates, no pre-sorting.
Semantic and table AI models read each vendor's format without per-template rules.
Fields are extracted and normalized to the client's taxonomy, stress fields included.
Deep links connect every value to its spot on the page for one-click verification.
Who it's for
QA reviewer
Operations leader
Product / data quality owner
A US-based advertising tracking company delivers actionable data on activity and spend across FCC-regulated broadcast and cable media outlets. The raw input: 100,000 pages of scanned invoice PDFs every day, across many vendors and templates. The incumbent solution was a fleet of desktop machines running OCR plus JavaScript parsing, backed by manual QA — in-house and on-shore. Then the desktop OCR product went out of maintenance, accuracy started deteriorating, and the company needed to switch. To evaluate replacements, they defined a set of stress fields — the extractions that break naive tools — and asked to see how each platform handled them.
Botminds built the model from base capabilities already integrated in the platform — semantic understanding, table AI, and related pre-trained models — instead of writing per-vendor templates. A taxonomy was created around the client’s stress fields, including the normalized values they needed downstream, so output arrived analysis-ready rather than raw.
In the engagement, 49 document types were automated across a set of 368 documents, with all 5 stress fields extracted — and every extracted value deep-linked to its exact location on the source page.
The client’s product is data about regulated media spend, so a wrong number surfaces with a customer before anyone in-house sees it. Deep linking changes the economics of quality control: instead of re-keying or hunting through a scan, a reviewer clicks from any value straight to the pixels it came from and confirms it in seconds. Template-free extraction handles vendor formats that keep changing, and audit stays fast even at 100,000 pages a day — which is what replacing a manual QA floor actually requires.
Objections, answered
Semantic and table models read the document rather than matching pixels to a template, and every extracted value is deep-linked to its exact location on the page — so a reviewer confirms any value in seconds. Low-confidence extractions route to review instead of flowing through unchecked.
That is how this client evaluated the platform: 5 stress fields designed to break naive extraction. All 5 were extracted, across 49 automated document types in a 368-document evaluation set, each value deep-linked to its source.
Nothing that needs engineering. Extraction is template-free — the models read each vendor's format from the document itself — so format changes and new vendors arrive without a parser to write or a template to maintain.
Deep linking changes the economics: instead of re-keying or hunting through a scan, a reviewer clicks from any value to the pixels it came from. Audit stays fast at 100,000 pages a day, which is what replacing a manual QA floor actually requires.
Watch the extractions that broke your last tool come out normalized — each value deep-linked to the pixels it came from.
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