Wavechat

Deploys a JavaScript widget that embeds directly into websites via a single script tag, eliminating the need for backend infrastructure or complex API integrations. The chatbot maintains conversation state within the browser session and communicates with Wavechat's cloud inference backend, handling natural language understanding and response generation without requiring developers to manage model hosting or scaling.

Provides a visual interface for uploading company-specific documents, FAQs, and web content that the chatbot uses as retrieval-augmented generation (RAG) context. The system automatically chunks and embeds documents into a vector database, then retrieves relevant passages during inference to ground responses in company knowledge without requiring users to write prompts or fine-tune models.

Maintains conversation history and context within a single browser session, allowing the chatbot to reference previous messages and build coherent multi-turn dialogues. Context is stored in browser memory and sent with each new user message to the inference backend, enabling the model to generate contextually-aware responses without explicit conversation state management by the developer.

Guides users through conversational lead capture by asking qualifying questions and extracting structured data (name, email, phone, intent) from natural language responses. The chatbot can pre-fill website forms with extracted information and trigger backend webhooks to send lead data to external systems, enabling basic lead routing without manual data entry.

Logs all chatbot conversations to a dashboard where users can view chat transcripts, user engagement metrics (message count, session duration, bounce rate), and export conversation data as CSV or JSON. Analytics are aggregated at the account level without per-user segmentation or cohort analysis, providing visibility into chatbot performance and user behavior.

Detects the user's language from incoming messages and responds in the same language using automatic translation or multilingual model inference. The system supports a predefined set of languages (likely 10-20 major languages) without requiring separate training or configuration per language, enabling global businesses to serve non-English-speaking customers with a single chatbot instance.

Allows users to define the chatbot's personality, tone, and communication style through a simple configuration interface (e.g., 'friendly and casual' vs 'professional and formal') without requiring prompt engineering or model fine-tuning. The system injects personality instructions into the inference prompt, shaping response generation to match brand voice without modifying the underlying model.

Assigns anonymous visitor IDs to users based on browser cookies or local storage, enabling the chatbot to track conversation history and engagement metrics across multiple sessions without requiring user login. The system correlates visitor IDs with conversation data to build anonymous user profiles, but does not integrate with CRM systems to identify users by email or account ID.