Intent Recognition

Off-the-shelf NLU tools give you a framework, but the accuracy comes from the intent taxonomy and training data you build on top of it, which is where most DIY implementations fall short. We design the taxonomy from your actual conversation logs and handle the harder cases — overlapping intents, multi-intent messages, out-of-scope detection — that generic templates don't cover well.

We build confidence thresholds so low-confidence classifications trigger a clarifying question or route to a human rather than the system guessing and acting on a wrong intent. We also set up a feedback loop so misclassifications get logged and used to retrain the model over time.

Yes, multi-intent detection is something we specifically design for, since real customer messages often combine a question with a complaint or a request with a cancellation. The system identifies each intent present and can route or sequence responses accordingly instead of only acting on the first one detected.

Existing conversation logs, support transcripts, or chat history speed things up significantly and improve accuracy, but we can also start from a structured intent taxonomy based on your business processes if you don't have data yet. In that case we build in a faster iteration cycle post-launch to correct the taxonomy against real usage.