NLP Solutions

For well-defined tasks like classification, NER, or sentiment analysis, a fine-tuned smaller model (e.g. DistilBERT or a similar encoder) is usually cheaper and faster at inference than calling a large hosted LLM, and often more accurate because it's trained specifically for the task. We reserve general-purpose LLMs for open-ended generation and reasoning, and use purpose-built NLP models for classification and extraction tasks with fixed label sets.

Keyword search matches literal terms; semantic search uses embeddings to match meaning, so a query for 'cancel my plan' can surface a document titled 'how to end your subscription' with no words in common. In practice we usually combine both (hybrid search) because pure semantic search can miss exact-match cases like SKU numbers or error codes that keyword search handles better.

It depends on the task and how similar it is to what a pretrained model has already seen. Simple binary classification can work with a few hundred labeled examples per class using transfer learning; nuanced multi-class or domain-specific extraction tasks typically need low thousands. We start with what you have, run a baseline, and tell you concretely whether more labeling will move accuracy before you invest in it.

We use multilingual embedding models (e.g. multilingual-e5 or LaBSE) so search and classification work across languages without training a separate model per language, and add machine translation only where the task genuinely requires generating text in the target language rather than just matching or classifying it.