Robofy

Processes incoming customer messages in multiple languages and routes them through a language detection pipeline before generating contextually appropriate responses. The system likely uses language identification models (possibly fastText or similar) to detect the customer's language, then either translates to a canonical language for processing or maintains separate language-specific response chains. Responses are generated in the detected language without requiring manual translation setup per language pair.

Operates a continuously running chatbot agent that intercepts incoming customer messages and attempts to resolve common support queries without human intervention. The system uses pattern matching or intent classification (likely via fine-tuned LLM or rule-based routing) to categorize incoming queries and match them against a knowledge base of pre-written or dynamically generated responses. Unresolved queries are escalated to human agents or queued for asynchronous handling.

Provides a pre-built, embeddable chat widget that integrates into websites via a single script tag or iframe injection, eliminating the need for custom frontend development. The widget handles UI rendering, message persistence, and communication with Robofy's backend via WebSocket or polling. The deployment likely uses a CDN-hosted JavaScript bundle that injects the chat interface into the DOM and manages session state client-side.

Generates chatbot responses by retrieving relevant information from a knowledge base (FAQ, documentation, or product information) and synthesizing it into natural language responses. The system likely uses semantic search or keyword matching to find relevant knowledge base articles, then passes them as context to an LLM to generate a coherent response. The knowledge base can be populated manually via a dashboard or automatically indexed from existing documentation.

Maintains conversation state across multiple message exchanges, allowing the chatbot to reference previous messages and build context for multi-turn conversations. The system stores conversation history (likely in a database indexed by session ID or customer ID) and retrieves relevant context when generating responses. Session management handles user identification (via cookies, localStorage, or explicit login) and conversation lifecycle (creation, continuation, archival).

Routes conversations to human support agents when the chatbot cannot resolve a query or when the customer explicitly requests human assistance. The escalation logic likely uses intent classification or confidence scoring to determine when to hand off, and integrates with ticketing systems or live chat platforms to queue conversations for agent pickup. The handoff preserves conversation context so agents have full visibility into the conversation history.

Provides a web-based dashboard for non-technical users to configure and customize the chatbot without code. The dashboard allows users to upload knowledge base content, define conversation flows, set response templates, and configure escalation rules. The system likely uses a low-code or no-code interface with drag-and-drop workflow builders or form-based configuration, abstracting away the underlying LLM and backend complexity.

Collects metrics on chatbot performance and customer interactions, providing dashboards and reports on conversation volume, resolution rates, customer satisfaction, and common query types. The system likely tracks events (message sent, query resolved, escalated, etc.) and aggregates them into metrics displayed in a dashboard. Analytics may include sentiment analysis or customer satisfaction scoring derived from conversation content.