Natural Language Processing (NLP) services

Often not for prototypes: an LLM prompt gets you a working classifier in an afternoon. At scale, though, a small fine-tuned model is cheaper, faster and more consistent for narrow tasks like routing or entity extraction. We usually start with an LLM to label data and validate the idea, then decide whether to distil.

Less than most teams expect. With LLM-assisted labelling and transfer learning, a few hundred well-chosen examples per category is often enough for a first model, and we improve from production feedback. If labels do not exist, we help design a labelling process your team can sustain. Active learning, where the model picks the most informative examples to label next, keeps the effort focused where it changes results.

Yes. Multilingual transformer models and modern speech models cover most major languages, and we test accuracy per language on your data. Where a language is under-served we combine translation, language-specific models or targeted fine-tuning. Right-to-left scripts, transliterated text such as Roman Urdu, and code-switching are common in our clients’ data and are tested explicitly.

Modern models perform very well on clear audio and degrade with noise, crosstalk and heavy accents. We measure word error rate on a sample of your recordings, add domain vocabulary and choose the model accordingly, so you get a real number rather than a vendor claim. For telephony audio, which is compressed and narrowband, we test the specific codec you use because it changes results noticeably.

As a REST API your systems call in real time, as batch jobs that write to your warehouse, or directly into dashboards. Most projects include a monitoring view so you can see volumes, categories and confidence over time. Predictions are stored with the model version that produced them, so results can be audited and compared when the model is updated.