Juji

Automatically understands user intent and context from conversational input without requiring extensive training data or manual intent labeling. Uses cognitive AI to parse meaning from natural language queries and route them appropriately.

Provides a drag-and-drop canvas interface for designing multi-turn conversation flows with branching logic, conditional responses, and complex dialogue paths. Non-technical users can map entire conversation trees visually without writing code.

Connects chatbot to external knowledge bases, FAQs, or documentation to provide accurate, sourced answers. Automatically retrieves relevant information from knowledge sources to ground chatbot responses.

Authenticates users and retrieves their profile data to personalize chatbot interactions. Enables the chatbot to reference user history, preferences, and account information in conversations.

Seamlessly transfers conversations from chatbot to human agents when needed. Preserves conversation context and history during handoff to ensure continuity of service.

Maintains conversation context across multiple turns, remembering previous user inputs and bot responses to provide coherent, contextually relevant replies. Enables natural back-and-forth conversations rather than isolated Q&A exchanges.

Allows users to define and apply consistent brand voice, tone, and personality traits to chatbot responses automatically. The system adjusts response style across all conversations to match specified brand guidelines without manual prompt engineering.

Automates handling of common customer support inquiries by routing questions to appropriate responses or escalating to human agents when needed. Reduces support team workload by handling repetitive questions at scale.

Conducts conversational lead qualification by asking targeted questions and scoring prospect quality based on responses. Automatically qualifies or disqualifies leads and routes high-quality prospects to sales teams.

Conducts conversational surveys and user research by asking questions in a natural dialogue format and collecting structured responses. Gathers qualitative feedback and insights through engaging multi-turn conversations.

Enables complex conditional branching where chatbot responses vary based on user inputs, user attributes, conversation history, or external data. Supports if-then logic without requiring code.

Publishes completed chatbots to various channels and platforms with minimal configuration. Handles deployment across web, messaging apps, and other integration points without requiring backend infrastructure setup.

Tracks and analyzes chatbot conversations to provide insights into user behavior, conversation success rates, and performance metrics. Generates reports on common questions, user satisfaction, and conversation outcomes.