OCR Context captures text, reconstructs structure, and converts raw files into reliable data layers for search, automation, and downstream systems.
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Watch how our AI intelligently pulls business context from messy papers into your digital workflow.
Tax Invoice
Sterling & Partners LLP
102 938 4756
Bill to
John Doe
Date
12 Mar 2026
Payment: Net 30
$48,750.00Structured context
Fields populate automatically from source documents.
Under the hood
Most tools run one engine and hope. This is the whole path a document takes, including the parts built to stop the AI from being creative.
Scans, phone photos and PDFs enter a Postgres-backed queue, so a slow document is never cut short by a request timeout.
The document picks its own engine. A digital PDF is read straight from its text layer and never touches a GPU.
Refinement runs only where OCR can be wrong, and identifiers are hidden from the model before it starts reading.
Text is chunked, embedded into pgvector so the document becomes chattable, and exported as TXT, DOCX or PDF.
Engine routing
Nothing is sent to a GPU that does not need one, and nothing is given up on after a single attempt.
pdf_text_layerExact characters. Refine is skipped entirely so emails and account numbers cannot be "corrected".
ocrLines and blocks detected on the page image, on an L4 GPU.
vision_handwritingPrimary handwriting path, chosen automatically when a page yields too little text.
visionAn image that still reads short after every other attempt, handwriting included. Last resort before a document is reported as failed.
Fidelity guards
Emails, URLs and banking identifiers are swapped for frozen tokens before refinement and put back afterwards, so a plausible-looking correction can never reach them.
Invoice to jonn@sterl1ng-partners.exampleInvoice to {{OCRLIT0}}Invoice to jonn@sterl1ng-partners.exampleLeft to itself, the model would tidy that address into one that never appeared on the page.
Drift guard
Refined output is compared back against the source on line count, word overlap and length. When it wanders too far the refinement is discarded and the raw OCR text is what you receive — a rougher result is still better than an invented one.
Built for real-world workloads
A creased receipt and a signed contract fail in opposite ways. Each one is routed differently — and only one of them should ever be handed to a language model.
The one document type with a dedicated extractor. Header fields and line items come back typed — quantities reconciled against unit price and total — and a whole batch lands in a single spreadsheet.
Input
TAX INVOICE · SP-2026-0412
Sterling & Partners LLP
Bill to John Doe Date 12 Mar 2026 Tax ID 1029384756 Retainer — Mergers & Acquisitions 41,000.00 VAT 20% 7,750.00 TOTAL DUE 48,750.00
Scan or phone photo
Output
extracted.json{
"supplier_name": "Sterling & Partners LLP",
"invoice_number": "SP-2026-0412",
"invoice_date": "2026-03-12",
"tax_id": "1029384756",
"currency": "USD",
"tax_rate": "VAT 20%",
"total_amount": 48750,
"line_items": [
{
"description": "Retainer — Mergers & Acquisitions",
"quantity": 1,
"unit": "service",
"unit_price": 41000,
"tax_rate": 20,
"total": 41000
}
]
}ocrinvoice extractorXLSXStructured output
Extraction is only half the job. What comes back has to open in Word, land in a spreadsheet, or be retrievable by a model — without anyone re-parsing it by hand.
MASTER SERVICES AGREEMENT
Executed 12 March 2026 between Northwind Systems Ltd
("the Supplier") and the Client named in Schedule 1.
13. TERM AND TERMINATION
13.1 This Agreement continues for twelve (12) months
from the Effective Date.
13.2 Either party may terminate this Agreement on
sixty (60) days written notice.
14. GOVERNING LAW
14.1 This Agreement is governed by the laws of
England and Wales.The refined text exactly as the pipeline produced it — line breaks and indentation intact, nothing added.
No public API yet — everything above is produced from the dashboard. When there is one, it will show up here as another tab.
What you can hold us to
No invented accuracy percentage, no latency number nobody measured. Every line below is a decision that exists in the code.
Billing that matches reality
Quota is spent at the end of a successful run, never at the start of an attempt. A document that fails costs nothing, and one that does not fit your remaining pages is rejected whole rather than processed halfway.
invoice-batch.pdfcompleted12 pagesscan-004.jpgfailed0 pagesagreement.pdfcompleted3 pages11
languages, chosen not guessed
You pick the language at upload and the pipeline commits to it. Nothing is auto-detected, so a Turkish invoice is never read as if it were English.
4
engines in the fallback ladder
Text layer, PaddleOCR on an L4 and Google Vision — with OpenAI Vision behind them before anything is called a failure.
Your documents go to inference endpoints and come back. They are not training data, ours or anyone else’s.
Payments run through Polar as merchant of record. No card number ever touches this application, so there is nothing here to leak.
Postgres decides what belongs to whom, not the application layer. Privileged columns are guarded by a database trigger on top.
The difference, and the details
Seven places where reading a document properly differs from running it through a recogniser once.
Ordinary OCROne engine for every page, whatever the page is.
OCR ContextRouted per document across four engines, with fallbacks behind each.
Ordinary OCRRendered to an image and re-recognised, so characters get guessed.
OCR ContextRead from the existing text layer, exactly, with no OCR step at all.
Ordinary OCRSilently "corrected" into something that looks plausible.
OCR ContextMasked before the model sees them and restored afterwards, untouched.
Ordinary OCRYou receive whatever it produced and have to spot it yourself.
OCR ContextA drift check discards the refinement and keeps the raw OCR text.
Ordinary OCRUsually out of scope, or returned as noise.
OCR ContextGoogle Vision first, OpenAI Vision behind it, retried automatically on low yield.
Ordinary OCRA text file, and the rest is your problem.
OCR ContextTXT, DOCX, PDF, spreadsheet export — and a document you can ask questions.
Ordinary OCRCounted against your allowance anyway.
OCR ContextNever charged. Quota is spent only after a run succeeds.
Ten pages a month, free, with no card. See what it does to your worst-scanned document before you decide anything.