Ground truth for every document
Turn any document — scans, PDFs, tables, entire folders — into structured data you can trust. No templates at ingest: every file is read completely first, what you use it for comes after.
Private by design
Your documents run on a dedicated instance. Nothing is pooled, shared, or used to train models. Ever.
No templates at ingest
Every file is read completely and faithfully first — text, tables, visuals, the lot. What you use it for is decided after, never baked into extraction.
Every value, cross-checked
Independent reads are diffed cell-by-cell and verified back against the source image. Disagreements are named and flagged — never silently guessed.
No per-page fees
Flat, predictable pricing. Process a million pages without a metered bill scaling against you.
The verification layer is open
Cross-read table diffing, VLM bounding-box grounding, and NLI-based hallucination rescue — three standalone pieces pulled out of Groundtruth's pipeline, MIT licensed. Use them in your own extraction or RAG system, no strings attached.
github.com/gauravmeena41/groundtruth-verify →from groundtruth_verify import verify_table_region
read_a = "| Acct | Amount |\n|---|---|\n| LN-1 | 1,00,000 |"
read_b = "| Acct | Amount |\n|---|---|\n| LN-1 | 100000 |"
result = verify_table_region(read_a, read_b)
# {"has_table": True, "diff_count": 0, "cell_diffs": []}
# "1,00,000" vs "100000" normalize equal — not a real diff.From document to data in three steps
One pipeline for clean digital PDFs and the worst photocopies — routed automatically, verified at every step.
Split & read
A bundle of files is broken into its logical documents and labeled, then every page is read — text, tables, and visuals like signatures, stamps, and maps.
Verify & link
Each read is checked back against the source image, then entities are matched across every document in the case — with conflicts surfaced, not hidden.
Review in minutes
A guided review — scan beside data, click-to-source, flag-jumping — brings each case down to 5–10 minutes, not three hours.
What comes out the other side
Not a mockup — the actual verification queue and entity model Groundtruth produces on real documents.
Cases reviewed in 5–10 minutes, down from three hoursEvery flag names its exact reason — nothing is silently guessed.
1 table cell(s) differ between reads — review
OCR vs ensemble disagree (agreement 0.07) — candidates surfaced for review
confidence 0.40 below floor 0.6
repeated-character run
OCR produced no text for this page
Every document is parsed into typed, structured entities automatically.
Built for the teams drowning in paper
If your documents are sensitive, high-volume, or messy, Groundtruth is built for you.
Legal
Sanction letters, KYC, deeds — flag owner-name mismatches against KYC, missing title deeds, deceased-owner records, and export claim amounts.
Finance
Vendor invoices — 3-way match purchase order, invoice, and receipt, then export straight to accounting.
Records
Archives and back-files — digitize decades of paper into searchable, structured data.
Healthcare
Intake forms and lab results — capture values and units, flag abnormal readings, without sending PHI to the cloud.
Your documents never leave your control
Cloud OCR APIs pool your most sensitive documents on shared infrastructure. Groundtruth doesn't. Every deployment is isolated, and your data is never used to train anything.
Dedicated instance
Your data is isolated to your own environment — no shared multi-tenant pool.
No training on your data
Documents are processed and returned. They never enter a training set.
Compliance-friendly
Built for teams that can't send sensitive documents to a public cloud API.