AI Chatbot Development

Gather Requirements

Business goals, supported channels, target users, and compliance constraints are identified to define the chatbot’s functional scope.

Collect Data

Historical chats, CRM records, and external datasets are aggregated to create comprehensive training material for language models.

Annotate Data

Intents, entities, and dialogue acts are carefully labeled to provide structured learning examples for accurate supervised model training.

Design Dialogues

Multi-turn flows, fallback mechanisms, and slot-filling strategies are crafted to ensure smooth, goal-driven conversational experiences.

Train Models

NLU and NLG models are trained with cross-validation, hyperparameter tuning, and transfer learning to enhance predictive accuracy.

Integrate APIs

External systems, messaging platforms, and CRMs are connected through secure API integrations for seamless and contextual interactions.

Test Thoroughly

Functional, load, and regression tests are executed to validate performance, accuracy, and user satisfaction across environments.

Deploy Production

The chatbot is deployed using container orchestration, CI/CD pipelines, and monitoring tools to ensure stability and scalability.