Built AI-native.
Reads any document,
any layout.
Not a template engine with a model bolted on. Extraction, routing, and verification all run through AI as a first-class step — so a scanned page, a flattened spreadsheet, or a table that breaks across ten pages comes out the other side as verified rows, not a guess.
| Item | Qty | Amount |
|---|---|---|
| Consulting | 10 | 12,000.00 |
| Data processing | 2 | 2,500.00 |
| Support | 1 | 1,200.00 |
Layouts change. Most tools don't notice.
Generic OCR tools read pixels, not tables — a subtotal row or a repeated header two pages later gets treated as new data.
"Upload PDF, get JSON" tools bolt a model onto a rules engine and call it AI. Nothing reconciles the output, nothing checks it, nothing tells you it's wrong.
A blank cell, an unreadable cell, and a zero all get flattened into the same 0 — silently, and downstream systems can't tell the difference.
How it works
One document, one continuous pass. Scroll to trace it through — ingest, route, normalize, verify.
Ingest & classify
Native spreadsheets are read directly. Born-digital PDFs go through text-layer extraction. Scanned or photographed pages route to a vision-language model — a first-class extraction tier, not a fallback bolted onto OCR.
Route to the right extractor
Each page type hits the extraction method built for it, decided by the pipeline itself, not one model forced across every input.
Normalize into rows
Differently-worded columns, split headers, and continuation rows collapse into one consistent structured schema.
Verify with reconciliation rules
Every row is checked against the document itself — subtotals, repeated identifiers, date sanity. Failures get a flag with a severity, not a silent wrong answer.
Whatever lands in the inbox.
Nine ways a document usually arrives — and the same pipeline for all of them, not a special case per format.
Multi-page ledgers
Column headers repeat, drop, or shift page to page. Rows keep their place anyway.
Flattened spreadsheets
A spreadsheet exported to PDF loses its structure on the page, not in the read.
Bank & account statements
Running balances, transaction rows, and summary totals reconciled against each other.
Structured applications
Field labels and values pulled by position and pattern, not a fixed template.
Multi-part submissions
A cover sheet, a form, and a schedule attached as one file — read as the parts they are.
Signed & dated pages
Signature blocks and dates located and checked against the rest of the document.
Photographed pages
Skewed, shadowed, phone-photographed — condition-corrected before it's read.
Faxed & re-scanned copies
Generation loss and degraded text routed to the model tier built for it.
Handwritten entries
Never a trusted single read — cross-footed or independently confirmed before it counts.
Every value traceable to a page and a box.
Every number reconciled against the document itself.
Nothing marked done until it's checked.
AI runs the extraction. AI runs the check on itself.
The same model that reads the page also checks its own work — calibrated confidence comes from independent reads agreeing, not one model's raw guess.
Provenance to the pixel
Every field carries its source page, bounding box, extraction method, and model version. Nothing is untraceable.
Blank, unparsable, and zero are not the same number
A cell that couldn't be read is never silently written as 0.
Confidence, calibrated
Confidence scores are checked against labeled ground truth, not reported as raw model probability.
Handwriting, never trusted on faith
A handwritten value only counts as a trusted read if it passes cross-field reconciliation or a second independent model read agrees exactly. Otherwise: flagged, not guessed.
Data shaped for the person reading it, not the model that produced it.
Trend rollups, large-item breakouts, frequency and severity views — structured for an analyst, not a raw JSON dump. Export to Excel, pull JSON, or hit the API directly.
| field | value | conf. | |
|---|---|---|---|
| invoice_number | INV-2024-1823 | 0.99 | ✓ |
| invoice_date | 2024-05-18 | 0.98 | ✓ |
| total_amount | 18,500.00 | 0.97 | ✓ |
| status | paid | 0.94 | ✓ |
Runs where your data already lives.
A clerk opens a forty-page scanned document, finds the data table starts on page 6, loses the header by page 9, and spends 15–40 minutes rekeying rows by hand — checking twice on anything handwritten. Multiply that by every document that lands this week.