Analyse, identify, classify, map, modify and ask. Most organisations buy these as four products from three vendors and spend a year gluing them together. DocxIntel ships them as one system that installs inside your own infrastructure, with the model weights in the bundle.
Accuracy is measured field-by-field against a human-adjudicated ground truth rather than character-by-character. The document mix, methodology and per-field breakdown are published in full on the accuracy page.
Each one is a separately addressable service behind your own API. They share a single document object, so nothing is re-parsed or re-guessed as work moves between them.
Resolve page structure, reading order, tables, stamps and handwriting across Arabic and English before any field is extracted. This is the layer every other capability depends on.
Extract named entities, key-value fields, table line items and contract clauses, each carrying its page number, bounding box and confidence score.
Determine what each document is, split multi-page packets into their constituent documents, and route each one to the queue that should handle it.
Link the same person, company, policy or asset across thousands of documents, reconcile the values they disagree on, and expose the resulting graph over your own API.
Redact, mask, watermark, convert and re-assemble documents. Redaction removes pixels and the underlying text layer rather than drawing a rectangle over them.
Ask questions in Arabic or English across the whole estate and get answers cited back to page and coordinates — or an explicit refusal when the documents do not support an answer.
The value is not in the individual capabilities. It is in the fact that a coordinate resolved during analysis is still attached to the value a regulator questions eighteen months later, because nothing was flattened into plain text along the way.
Because each step reads the output of the step before it rather than re-reading the original file, adding a capability adds intelligence instead of adding another parse.
This is a motor claim packet arriving as a single 40-page PDF from a broker. Every step runs on your hardware, inside your perimeter.
Forty pages of mixed scans and phone photos are deskewed, dewarped and contrast-corrected, then segmented into blocks and tables with reading order resolved — including the six pages that are Arabic and the two that are bilingual.
The single PDF becomes an Emirates ID copy, a driving licence, a police report, two repair invoices, a medical report, a claim form and a policy schedule. Each fragment gets a type, a confidence score and a downstream queue.
The invoices yield line items, VAT and totals. The police report yields incident date, location and plate numbers. The claim form yields the claimant details. Every value carries page, bounding box and confidence.
The claimant on the form, the name on the Emirates ID and the policyholder on the schedule are resolved to one entity despite three different transliterations. The repair total is reconciled against the policy limit and a mismatch is flagged.
A copy for the third-party repairer has the medical report and all personal identifiers truly redacted — pixels and text layer removed — watermarked, and accompanied by a redaction log listing every removal.
Was the vehicle covered on the incident date? Does the repair estimate exceed the policy excess? Each answer arrives with citations to the exact page regions it came from, and an explicit refusal when the packet does not contain the evidence.
The stitched approach is a reasonable engineering decision made three products too early. It usually costs more in integration than it saved in licence fees.
Vendors and contracts to manage
Where documents are processed
Coordinates preserved end to end
Shared entity model across documents
Answers cited to a page region
Cost of reprocessing the archive
True air-gapped operation
Who holds the model weights
Comparison reflects publicly documented behaviour of metered document-parsing and IDP products as of 2026, including credit-based per-page pricing and enterprise-gated self-hosting. Verify against current vendor documentation before making a decision.
What comes up when a platform team compares one system against a stack they would otherwise assemble themselves.
They arrive together. The licence is sized by deployment footprint — GPUs, nodes, environments — not by which capabilities you switch on, so there is no upsell path from parsing to extraction to Q&A. Most customers start by putting one capability into production and enable the rest as their use cases mature, without a new commercial conversation.
