Natural language processing
Your customers don't write like your training data. They abbreviate, misspell, switch languages mid-sentence, and describe your product using words you've never used for it.
Language is the messiest data you own
Every organisation is sitting on text. Support tickets, reviews, emails, call transcripts, case notes, contracts. It's usually the richest source of information in the business and the least used, because it doesn't fit in a column.
The difficulty isn't reading it. Modern models read text extremely well. The difficulty is that language carries meaning through context, tone, negation, and domain convention — and the phrase that means "urgent" in your industry might be entirely unremarkable in another.
Useful NLP is domain work as much as model work. We spend the early weeks learning how your people actually write.
What we build
- Classification and routing
- Sorting incoming text by topic, urgency, sentiment, or intent, so it reaches the right place without a person reading it first.
- Information extraction
- Pulling structured fields out of unstructured text: dates, amounts, parties, obligations, symptoms, specifications.
- Summarisation
- Condensing long documents, threads, or transcripts into something a person will actually read, with the important details preserved rather than averaged away.
- Semantic search
- Search that finds documents by meaning rather than keyword match, so "can't log in" surfaces the article titled "authentication troubleshooting."
- Analysis at volume
- Themes and trends across thousands of reviews, tickets, or survey responses. The questions you can't answer by reading a sample.
How we approach it
- We read your data first, properly
- Before any modelling, we go through a sample by hand with someone from your team. This is where we learn your abbreviations, your recurring edge cases, and the three ways your customers describe the same complaint.
- We define the labels with you
- Most NLP failure traces back to categories that seemed obvious in a meeting and turned out to be ambiguous in practice. If two of your own people label the same ticket differently, no model will do better. We settle that first.
- We check for bias in the sample
- Text data reflects who was writing and who was being written about. Where that skews results in ways that matter, we identify it rather than shipping it.
- We measure per-class, not overall
- A model that's 95% accurate overall can be useless on the rare category you built it for. We report performance for each class, especially the small ones.
Before you ask.
Yes, though quality varies by language and by how much of your data is in each. We'll be specific about what's achievable for your particular mix.
Knowledge Search (RAG)
Your organisation already knows the answer. It's in a PDF, in a folder, that someone left in 2022.
Intelligent SystemsDocument Processing
Somewhere in your business, a person is reading a PDF and typing what it says into a form. Possibly right now.
AI & MLGenerative AI
Most generative AI projects die somewhere between the demo and the deadline. We build the version that survives the gap.
Tell us what you're trying to build.
A 30-minute call, no charge and no pitch deck. Describe the problem and we'll tell you how we'd approach it, roughly what it costs, and whether we're the right team for it. If we're not, we'll say so.
30 minutes · No charge · No deck