NLU Intent Classifier — Custom Intent Recognition Model

I will build and train a custom intent classification model for your NLU system — covering intent taxonomy design, training data collection guidance, model training (fine-tuned transformer or Rasa NLU depending on your stack), evaluation with per-intent precision/recall, confusion matrix for similar-intent disambiguation, and integration into your application. Intent classifiers trained on too few examples per intent or with insufficient negative training data (examples of utterances that match no defined intent) produce overconfident models that assign every input to the nearest intent rather than correctly recognising out-of-scope queries.

The classifier covers intent taxonomy review, training data validation, model training with per-intent evaluation, confusion matrix analysis for near-intent disambiguation, fallback intent configuration, and integration guidance for your application stack.