Build Chatbot

Provides a drag-and-drop interface for non-technical users to construct conversation flows without writing code. The builder likely uses a state-machine or node-graph architecture where users define conversation branches, conditions, and responses visually. Each node represents a conversational turn or decision point, with edges representing user intents or input patterns. The system compiles these visual flows into executable conversation logic that routes user messages through the defined graph.

Enables deployment of a single chatbot across multiple messaging platforms (web widget, Facebook Messenger, WhatsApp, Telegram, etc.) through a unified backend. The system likely maintains a channel abstraction layer that translates between platform-specific message formats and a canonical internal message representation. When a user sends a message on any channel, the platform normalizes it, routes it through the conversation engine, and formats the response back to the originating channel's API.

Records all conversations in a queryable format and provides export capabilities for compliance, training, or analysis. The system logs every message, bot response, intent classification, and system action with timestamps and metadata. Conversations can be exported as transcripts (plain text, PDF, JSON) or accessed via an audit log interface. This enables compliance with data retention policies, training data collection for model improvement, and investigation of bot failures or user complaints.

Automatically categorizes incoming user messages into predefined intents (e.g., 'pricing inquiry', 'technical support', 'billing issue') using NLP-based text classification. The system likely uses either rule-based pattern matching (keyword detection, regex) or lightweight ML models (Naive Bayes, logistic regression, or small transformer models) trained on examples provided during bot setup. Classified intents are then mapped to corresponding conversation flows or response templates, enabling the bot to route messages to appropriate handlers without explicit user input.

Allows users to upload or link existing knowledge base content (FAQs, help articles, documentation) that the chatbot can search and reference when answering questions. The system likely implements a simple retrieval mechanism — either keyword matching against indexed documents or semantic search using embeddings — to find relevant articles when a user query matches a knowledge base topic. Retrieved content is then summarized or directly quoted in bot responses, reducing the need for manual response authoring.

Tracks and visualizes chatbot performance metrics including conversation volume, user satisfaction, intent distribution, and fallback rates. The system collects telemetry from every conversation — message counts, intent classifications, response times, user ratings — and aggregates this data into dashboards showing trends over time. Analytics likely include funnel analysis (where conversations drop off), common unresolved queries, and bot accuracy metrics, enabling users to identify improvement opportunities without technical analysis.

Enables seamless escalation from automated bot responses to human agents when the bot cannot resolve a query. The system detects escalation triggers (user frustration signals, intent confidence below threshold, explicit 'talk to human' requests) and routes conversations to available agents via email, Slack, or platform-native queue. Conversation history is preserved and passed to the human agent, providing context for faster resolution. The workflow may include queue management, agent assignment rules, and SLA tracking.

Maintains user context across multiple conversations, allowing the bot to reference prior interactions and personalize responses. The system stores user identifiers (email, phone, user ID) and associates conversation history with each user. When a returning user starts a new conversation, the bot retrieves prior context and can reference previous issues, preferences, or account details. Personalization may include dynamic response templates that insert user names or account information, or conditional logic that branches based on user history (e.g., 'returning customer' vs. 'new user').

Allows users to create response templates with variable placeholders that are dynamically filled with user data or conversation context at runtime. Templates support simple variable substitution (e.g., 'Hello {{user.name}}, your order {{order.id}} is ready') and conditional logic (e.g., 'if user.is_premium then show premium response else show standard response'). The system likely uses a simple templating engine (Handlebars, Jinja2, or custom) that processes templates during response generation, enabling personalization without requiring code.

Automatically extracts structured information (entities) from user messages such as names, dates, phone numbers, product names, or order IDs. The system uses pattern matching, regex, or lightweight NER (Named Entity Recognition) models to identify entities and map them to conversation slots. Extracted entities are stored in conversation context and used to populate forms, trigger actions, or provide information to downstream systems. For example, a user saying 'I want to return my order from last Tuesday' would extract 'return' (intent), 'last Tuesday' (date entity), and 'order' (object).

Enables the chatbot to trigger external actions or fetch data from third-party systems via HTTP webhooks. When a conversation reaches a specific point (e.g., user requests a refund), the bot sends a webhook payload to a user-configured URL with conversation context. The external system processes the request and returns a response that the bot uses to continue the conversation or provide information. This allows integration with CRMs, payment systems, ticketing platforms, or custom backends without requiring the platform to build native integrations.