Automatic categorisation, entity recognition and routing of content at scale.
Every organisation already runs a classification system. It is just informal: people read tickets and forward them, skim documents and file them, scan feedback and flag the angry ones. That works until the volume climbs. Then backlogs grow, labels drift, and two colleagues file the same document in three different places.
We build the automatic version. NLP and machine learning models categorise your content, recognise the entities inside it and apply consistent tags, covering everything from spam detection and sentiment through to content moderation. And because a classifier trained on messy, contradictory examples produces messy, contradictory labels, we start where we always start: the data core. Governed, structured data first, models on top.
The result is content that arrives sorted. Each item categorised, tagged and routed to the right queue, team or system, with the labelling logic written down where you can inspect it.
Content sorted automatically against your categories, from spam detection and sentiment to content moderation.
Models that identify the entities, themes and relationships inside your content and turn them into searchable, structured fields.
Every tag applied against a defined schema, with model-driven validation to remove the drift and human error of manual tagging.
Classified content moves to the right queue, workflow or system, so a label triggers an action rather than sitting in a column.
We find where classification pays off first, pinpointing the manual sorting, duplication and friction that eat your team's week.
We aggregate content from documents, messages and systems, then clean and normalise formats so the models see consistent input.
NLP, entity recognition and embedding models categorise the content and map it into defined schemas your systems can query.
We enrich each dataset with metadata and confidence scores, and verify quality, compliance and completeness before anything goes live.
Automated pipelines handle continuous ingestion, with versioning and lineage tracking built in so every label stays traceable and audit-ready.
Text, PDFs, emails, chat logs, audio transcripts and images: any content that lacks a defined structure. If people currently read it and sort it by hand, it is a candidate.
Every label is applied against a defined schema and checked with model-driven validation and confidence scores. Low-confidence items go to a person rather than being guessed at, and quality is verified before anything is deployed.
Yes. All pipelines include versioning, lineage tracking and compliance controls, so every label is traceable and the whole system stays audit-ready.
Yes. We design automated pipelines for continuous ingestion, so new documents, messages and records are classified and tagged as they come in.
It can, and we would argue it should. A tag that triggers an action, sending an item to the right queue, team or workflow, is worth far more than a tag that sits in a column.
If part of every week goes on sorting, labelling or forwarding content by hand, that is a classification problem, and in our experience it is usually a well-bounded one. Talk to us and we will look at what you are sorting, what the labels should be, and whether your data is ready to support them.
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