Summarise & extract

Document intelligence

Document intelligence that turns your documents, emails and PDFs into summaries and structured data you can act on.

Overview

Turning documents into data you can use

Document intelligence is the use of AI to read unstructured documents, emails, contracts, and PDFs and turn them into structured, usable data: summaries, extracted fields, classifications, and the entities inside them. Done well, it replaces hours of manual reading and retyping with a governed pipeline your systems can trust.

Most of what a business knows isn't sitting in a tidy table. It's in PDFs, emails, contracts, call transcripts, scanned forms, and chat logs, and none of it means much to a system built to read rows and columns. Document intelligence is how we close that gap. We take unstructured content and turn it into concise summaries and clean, structured data that your other systems can act on. If you want the fuller picture of what counts as unstructured data and where it tends to hide, we cover that in our guide to unstructured data.

This capability suits teams drowning in documents they don't have time to read properly: contract terms buried in PDFs, support tickets that need triaging, reports nobody has time to skim before a meeting. Instead of someone reading and retyping, a pipeline reads, classifies, and pulls out the facts that matter.

It only works if the data underneath is trustworthy, and that's Shipshape's starting point on every project. Extraction built on a shaky foundation just produces confident-looking nonsense faster. We build this on the same governed data core as everything else we deliver, so what comes out the other end holds up when someone checks it.

What you get

What you get

Automated classification

Classification, extraction, and normalisation applied across large volumes of documents, so nobody is retyping figures into a spreadsheet.

Concise, accurate summaries

Long documents condensed into the handful of sentences someone actually needs to make a decision.

Entities and relationships found

NLP and machine learning identify people, dates, amounts, themes, and relationships at scale.

Governed and audit ready

Every dataset is validated, versioned, and traceable, so it holds up when someone asks where a figure came from.

Proof

Proof it works

1.5
days of admin time freed each week at Smarter Services

Smarter Services, a facilities management company running over 300 sites, had a team spending a day and a half every week logging into seven separate systems and pulling the results together by hand into a report. Automating that consolidation freed up 1.5 days of admin time each week for the team to spend elsewhere.

Read the Smarter Services case study
Method

How we deliver

  1. Find where the value is hiding

    We look across your documents, inboxes, and systems for the unstructured content holding the most untapped insight, and where the friction and duplication actually sit.

  2. Collect and prepare

    We gather content from documents, messages, and systems, then clean and normalise the formats so they're usable.

  3. Classify and structure

    NLP, entity recognition, and embedding models sort, extract, and map information into schemas your systems can read.

  4. Enrich and validate

    Datasets get metadata, relationships and confidence scores, checked for quality and completeness before anything ships.

  5. Scale and govern

    Pipelines run continuously as new content arrives, with governance built in so every dataset stays accurate and traceable.

FAQ

Questions we hear most

What is document intelligence?

Document intelligence is the use of AI to read unstructured documents, emails, contracts, and PDFs and turn them into structured, usable data: summaries, extracted fields, classifications, and the entities inside them. It replaces manual reading and retyping with a governed pipeline your systems can trust.

What kinds of unstructured content can you work with?

We handle text, PDFs, images, audio transcripts, emails, chat logs, and more, anything that doesn't already come in a defined structure.

How does this improve our AI and analytics?

Structuring your data improves model accuracy, cuts training time, and makes the resulting insight usable across teams rather than stuck in one document.

Is governance built in?

Yes. Every pipeline includes versioning, lineage tracking, and compliance controls, so the output is audit ready.

How fast can we see results?

Most clients have usable, structured datasets within 2 to 4 weeks of the project starting.

Do you support ongoing, continuous ingestion?

Yes, we design pipelines that keep datasets updated automatically as new content arrives, not just a one-off clean-up.

Extraction only earns its keep if what comes out the other end is accurate enough to act on. Talk to us about the documents and data sitting untouched in your business, and we'll tell you plainly what's worth structuring first.

Start at your core.

Tell us where your data is today and what you want AI to do. We'll come back with a straight answer on what your foundation needs and where the quickest real win is.

Talk to us