What We Build

Document Intelligence & Automated Document Processing

Document intelligence is the automated conversion of unstructured documents — contracts, filings, invoices, reports, clinical notes — into structured, queryable data, with each extracted field traceable back to the exact place in the source document it came from.

Most enterprises sit on an enormous amount of information that no system can query, because it is locked in prose. The obligations are in the contracts, the risk is in the filings, the operational truth is in the field reports — and every one of them has to be read by a person before anything can be done with it. That reading is expensive, slow, inconsistent between reviewers, and almost impossible to scale during a peak.

Document intelligence removes the reading bottleneck without removing the reviewer. The system extracts the fields that matter, normalises them into a schema your downstream systems can consume, scores its own confidence on each one, and routes only the uncertain cases to a human. The reviewer stops reading everything and starts reviewing exceptions.

The traceability requirement is what separates an enterprise system from a demo. Every extracted value carries a pointer to the page, paragraph and span it came from, so an auditor asking why a figure is what it is gets an answer rather than a shrug.

What we build

Extraction pipelines are designed around the document families you actually receive, including the badly scanned and inconsistently templated ones, rather than around an idealised sample.

Layout-aware parsing that handles multi-column text, nested tables, footnotes and appendices without scrambling them
OCR for scanned and photographed documents, with quality scoring that flags pages too degraded to trust
Schema-driven field extraction normalised to the structure your ERP, CLM or data warehouse expects
Per-field confidence scoring, with thresholds that decide what passes straight through and what a human sees
Clause and obligation classification for agreements — renewal terms, liability caps, change-of-control, termination triggers
Cross-document reconciliation, catching where two documents that should agree do not
Full provenance on every value, down to the page and text span

Human review, kept where it earns its place

The goal is not an unattended system. It is a system where human attention is spent on the ten per cent of cases that are genuinely ambiguous rather than spread thinly across all of them. We build the review interface as part of the delivery, because an extraction pipeline without a good exception queue just relocates the bottleneck.

Reviewer decisions feed back as training and evaluation data, so the share of documents needing review falls over the life of the system. That curve is tracked and reported rather than assumed.

How it integrates

Extracted data is only useful where the work happens. Output lands in the systems your teams already use — the contract repository, the ERP, the data warehouse, the case management tool — through their APIs, on the schedule your process runs on. Where a downstream system has no usable API, we deliver into a staging table your integration layer can read.

02Where It Applies

Typical applications of Document Intelligence.

Contract obligation extraction and renewal-date tracking across a full portfolio

IPO prospectus and regulatory filing review

Loan file and credit underwriting document automation

Clinical trial documentation review and cross-referencing

Trade and customs documentation processing

Invoice, purchase order and goods-receipt three-way matching

Adverse event detection from unstructured clinical notes

03Questions

Document Intelligence, answered.

The questions that come up on almost every first call about this capability.

How accurate is automated extraction?

It varies by field and by document quality, which is exactly why per-field confidence scoring matters more than a single headline accuracy number. A contract's effective date on a clean digital PDF is close to solved; a hand-annotated liability cap on a fax-quality scan is not. A well-designed system knows the difference and routes accordingly. We establish the real figures on your documents during discovery, against a human-labelled sample, before anyone commits to a target.

Do we have to standardise our templates first?

No. Template standardisation is a multi-year organisational project and systems that depend on it tend never to ship. Extraction is built to handle the variation you actually have, including documents originating outside your organisation where you have no control over format at all.

What happens when the system is not sure?

It says so, rather than guessing. Low-confidence fields are flagged and routed to the review queue with the relevant part of the source document already open at the right place. The threshold is a business decision, not a technical one — a field feeding a regulatory submission warrants a lower tolerance than one feeding an internal dashboard, and the system is configured per field accordingly.

Can this handle documents in more than one language?

Yes. Multilingual extraction is well supported, though accuracy varies by language and by how much of your evaluation data covers it. If a meaningful share of your corpus is non-English, we build that into the evaluation set from the start rather than discovering the gap in production.

Is this different from the OCR we already have?

OCR turns an image of text into text. That is the first step and on its own it produces an unstructured wall of characters. Document intelligence is what happens next: understanding the layout, identifying which text is which field, classifying clauses, normalising values into a schema, scoring confidence and preserving provenance. If you already have good OCR, that is a useful head start rather than a duplicate.

04Sectors