Stop paying people to retype documents
Custom intelligent document processing that classifies, reads, and validates invoices, claims, contracts, and forms, then writes clean data straight into your systems.
How it works
Traditional OCR works until the vendor changes their invoice template. Language models read documents the way a person does, understanding that this number is the net total and that clause is an auto-renewal, whether the layout has been seen before or not. We combine both, add validation against your own records, and route only genuine exceptions to a human. Most teams find the review queue shrinks to a few percent of volume within the first month.
Reads documents it has never seen before
Layout-agnostic extraction that handles new vendors and formats without a new template.
- Scans, photos, PDFs, email attachments, and handwriting
- Tables, line items, stamps, signatures, and multi-page splits
- Multilingual documents processed in a single pipeline
- No per-template configuration to maintain as formats change
Classify, extract, validate, route
A full pipeline, not just an extraction API, so documents arrive as finished transactions.
- Auto-classification by document type on intake
- Cross-checks against POs, contracts, and master data
- Confidence thresholds you set per field, not per document
- Posts directly to your ERP, CRM, or claims platform
An audit trail your finance team will accept
Every extracted value traces back to the exact spot on the page it came from.
- Field-level provenance with page and bounding-box reference
- Reviewer actions and overrides logged in full
- PII and PHI redaction with configurable retention
- SOC 2 aligned hosting in your cloud or a dedicated tenant
Where teams use it
Built for real revenue work.
Accounts payable without the keying
Invoices arrive by email, get matched to the purchase order, and post to the ERP automatically. Only mismatches and unknown vendors reach the AP clerk.
Claims and application intake
Forms, IDs, and supporting evidence are classified, extracted, and completeness-checked at submission, so adjusters open a file that is already assembled.
Contract and lease abstraction
Pull renewal dates, payment terms, liability caps, and termination clauses out of thousands of agreements into a searchable register your legal team can actually query.
“We were keying about 14,000 invoices a month across two shifts. Six weeks after go-live, four percent needed a human and the rest posted on their own. The team moved to vendor management instead of data entry.”
FAQ
Questions, answered.
Can AI read and process documents?
Yes, and it now handles the cases that broke older tools: unfamiliar layouts, poor scans, handwriting, mixed languages, and line-item tables. A modern pipeline classifies the document, extracts the fields you care about, checks them against your own records, and flags anything below your confidence threshold for a person. On high-volume structured documents like invoices, teams typically see 90 to 97 percent processed with no human touch.
How is this different from the OCR we already have?
OCR converts pixels into characters; it doesn't know which characters are the tax amount. Template-based tools add rules per layout, which is why they break whenever a supplier redesigns a form. Our systems interpret the document semantically, so a new vendor works on day one, and we still use OCR under the hood where it is the more accurate and cheaper option for clean printed text.
What accuracy can we expect, and what happens when it's wrong?
Field-level accuracy is typically 98 to 99.5 percent on production traffic once the pipeline is tuned, and we measure it against a labeled sample from your own documents before launch. Accuracy matters less than what happens at the edges: every field carries a confidence score, and anything under the threshold you set goes to a review queue where a person confirms it in seconds with the source region highlighted. Corrections feed back in to improve subsequent runs.
What does an IDP project cost and how long does it take?
Typical builds run $35,000 to $120,000 fixed, driven by document variety, the systems you post into, and compliance requirements. A single-document-type pipeline into one ERP is often live in five to six weeks; a multi-type intake with human review workflows and audit reporting takes ten to twelve. Running costs are usually a few cents per page, and we'll model your break-even against current processing cost before you commit.
Keep exploring
More from the Leadify platform.
Custom AI Software
Production AI built to your requirements, delivered at a fixed cost you own outright.
Generative AI Development
Once documents are structured, make them answerable in plain language.
NLP in CRM — ask your data anything
How extracted document data becomes a question your team can just ask.
Send us 50 of your worst documents
We'll run them through a working pipeline and show you real extraction accuracy before you spend anything.