Documents, emails and PDFs turned into summaries and structured data you can act on.
Most of what a business knows is not sitting in a tidy table. It is 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. Summarise and extract 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 do not 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 is 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.
Classification, extraction and normalisation applied across large volumes of documents, so nobody is retyping figures into a spreadsheet.
Long documents condensed into the handful of sentences someone actually needs to make a decision.
NLP and machine learning identify people, dates, amounts, themes and relationships at scale.
Every dataset is validated, versioned and traceable, so it holds up when someone asks where a figure came from.
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 studyWe look across your documents, inboxes and systems for the unstructured content holding the most untapped insight, and where the friction and duplication actually sit.
We gather content from documents, messages and systems, then clean and normalise the formats so they are usable.
NLP, entity recognition and embedding models sort, extract and map information into schemas your systems can read.
Datasets get metadata, relationships and confidence scores, checked for quality and completeness before anything ships.
Pipelines run continuously as new content arrives, with governance built in so every dataset stays accurate and traceable.
We handle text, PDFs, images, audio transcripts, emails, chat logs and more, anything that does not already come in a defined structure.
Structuring your data improves model accuracy, cuts training time and makes the resulting insight usable across teams rather than stuck in one document.
Yes. Every pipeline includes versioning, lineage tracking and compliance controls, so the output is audit ready.
Most clients have usable, structured datasets within 2 to 4 weeks of the project starting.
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 will tell you plainly what is worth structuring first.
Tell us where your data is today and what you want AI to do. We will come back with a straight answer on what your foundation needs and where the quickest real win is.
Talk to us