NLU focuses on comprehension—interpreting intent, entities, and meaning from text. NLP is broader (generation, translation, summarization). NLU powers intent classification for chatbots, slot filling for conversational AI, and semantic search.

We use multilingual models (XLM-R, mBERT) and fine-tune on your domain data. We support code-switching and low-resource languages. We also build language-agnostic intent schemas so one model can serve multiple languages.

Yes. We integrate NLU with ASR output for voice assistants. We use dialogue state tracking and context windows so the model understands multi-turn conversations. We handle corrections, clarifications, and follow-up questions.

With sufficient domain data, we typically achieve 90%+ intent accuracy. We use active learning to prioritize labeling, and we add confidence thresholds and fallbacks. We also provide ongoing evaluation and retraining as data shifts.

Yes. We build custom NER for product names, dates, locations, and domain-specific slots. We combine rule-based patterns with neural models. We also handle composite entities and normalization (e.g., "next Tuesday" → ISO date).

We deploy via REST APIs, serverless functions, or embedded in chatbot platforms. We optimize for latency (ONNX, TensorRT) and scale with Kubernetes. We add monitoring, A/B testing, and versioning for safe updates.

A typical project takes 4–8 weeks: data collection and annotation, model training and evaluation, integration with your stack, and deployment. Complex multi-intent or multilingual projects may take longer. We provide incremental milestones and demos.