We are hiring across engineering, design and delivery.
See open rolesBusiness text is nothing like the clean corpora models are benchmarked on. It has typos, abbreviations, three languages in a sentence, and a table pasted in the middle of an email.
We build for that. Extraction that survives a supplier changing their invoice layout, classification that handles the categories your business actually uses rather than generic ones, and search that finds the right clause even when the words do not match.
Fields pulled from invoices, contracts and forms, with confidence scores per field.
Routing and tagging against your own taxonomy, not an off-the-shelf one.
Finding by meaning rather than keyword, across documents and tickets.
What customers are actually saying at volume, grouped rather than counted.
Hindi, regional languages and mixed-script text, which most tooling handles badly.
Low-confidence extractions queued for a person, with the source shown alongside.
If one of these sounds like your situation, it is worth a conversation.
A team reading PDFs and entering the same fields into a system all day.
Thousands of reviews and survey comments, summarised by whoever has time.
Keyword search over documents where the useful clause never contains the word searched.
The difference between a model that helps and one that quietly misleads.
We measure how well the current process does first. Surprisingly often it is the thing to beat, and sometimes it wins.
Held-out, time-split where time matters. An accuracy figure from the training set is not a result.
Confident cases go through; uncertain ones go to a person. That boundary is set by you, not by us.
Data drifts, behaviour changes, accuracy decays. You get the alerting, not just the model.
Machine learning, generative AI, automation and analytics — applied to problems where the numbers justify it, and measured after launch.
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Deliberately front-loaded with the question of whether to proceed at all.
We look at your actual data, establish the baseline, and give you a straight answer on whether this is worth building. Some of these end in "do not".
A working model measured against the baseline on held-out data, with the error cases in front of you rather than averaged away.
Integration into the system where the decision is actually made, with the human-review boundary, logging and rollback in place.
Drift detection, periodic re-evaluation, and retraining when the numbers say so rather than on a calendar.
Tell us what you are trying to do. If NLP solutions is not the answer, we will say so.
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