Text analysis, entity extraction and NLP classification
Make sense of unstructured text at scale.
The challenge
Tickets, emails, contracts and reviews pile up faster than anyone can read them. The insight and the obligations buried in that text never get used.
We build NLP pipelines that classify, extract and summarise text — from support tickets and contracts to reviews and emails — so unstructured language becomes structured, searchable data your systems can act on.
The outcome
Structured insight from text nobody had time to read.
The concrete things this delivers once it is running in your business.
Auto-tag and route text by topic, intent or priority.
Pull names, dates, amounts and clauses into structured fields.
Condense long threads and documents into the parts that matter.
Track how people feel and what they keep raising.
Process content across the languages your business operates in.
Structured output flows into your CRM, ticketing or data store.
A staged path so value is proven before the next investment.
We agree exactly what should be extracted or classified.
A quick pass on real samples to prove accuracy is achievable.
Robust processing wired into your systems.
Measured against a labelled set before it goes live.
Monitored and adjusted as language and volumes shift.
Auto-tagging and routing inbound messages
Extracting clauses and terms from contracts
Sentiment and theme analysis on feedback
Straight answers. If yours isn’t here, ask us.
We validate against a labelled sample and report accuracy honestly. Uncertain cases can be routed for human review.
Yes — we tune to your domain language and validate on your real text, not generic samples.
Yes, across the languages you operate in. We confirm coverage for your specific languages during scoping.
Straight into your systems — CRM, ticketing, data store — so the structured output is actually used.
Scoped to the document types and volume, with a fixed quote after a prototype.
Book a call and we’ll scope it against your processes and ROI — or email us directly.