YourGPT

Ingests training data from heterogeneous sources (websites via URL/sitemap crawling, PDFs, Word docs, CSVs, Notion links, YouTube videos, raw text) and stores them in a RAG-compatible vector index. The 'Auto ReIndex' feature monitors source content for changes and automatically updates the knowledge base without manual re-upload, enabling dynamic knowledge synchronization. Implementation uses document chunking and embedding generation (model unspecified) to support semantic retrieval during conversation.

Provides a visual drag-and-drop interface for designing multi-turn conversation flows without writing code. Flows support sequential step execution, intent detection (classifying user queries), conditional branching, form capture, API calls to external services, and custom code execution within steps. Each step can trigger actions (send message, call API, execute code) and route to subsequent steps based on conditions, enabling complex conversation logic without backend development.

Exposes REST API endpoints (Professional+ tier) and webhook support for programmatic chatbot management, conversation triggering, and event handling. Developers can create custom integrations beyond the pre-built channel connectors, automate chatbot configuration, or build custom workflows that respond to external events. Webhook payloads include conversation context, allowing external systems to react to chatbot events.

Claims a 'Self Learning' feature that automatically refines the chatbot's knowledge base and response quality based on conversation outcomes. Implementation mechanism unknown, but likely involves tracking which responses were marked as helpful/unhelpful by users or agents, and using that feedback to adjust response generation or knowledge base weighting. May also involve automatic intent detection improvement based on conversation patterns.

Provides tools to rewrite or rephrase chatbot responses before sending, allowing agents or administrators to adjust tone, clarity, or content. Likely includes templates or suggestion mechanisms to help craft better responses. May also support automatic rephrasing to match brand voice or tone guidelines.

Allows creation and management of pre-written response templates ('canned replies') that agents can quickly insert into conversations. Templates can include variables (e.g., {{customer_name}}, {{order_id}}) that are automatically populated from conversation context. Reduces response time for common questions and ensures consistency across support team.

Allows support agents and team members to add internal notes to conversations that are visible only to the team, not to customers. Notes are preserved in conversation history and visible during human handoff, providing context for agents taking over from the chatbot. Metadata (tags, priority, department) can be attached to conversations for organization and routing.

Allows export of conversation transcripts in email-friendly format and automatic delivery via email to specified recipients. Transcripts include full conversation history, internal notes, and metadata. Useful for compliance, record-keeping, or sharing conversation context with external parties.

Supports creation of rich message formats beyond plain text, including interactive buttons, card layouts, carousels (scrollable lists), and form elements. These rich messages render natively on supported channels (Slack, Discord, WhatsApp, etc.) with platform-specific formatting. Users can click buttons to trigger actions (navigate flow, call API, submit form) without typing.

Detects when a conversation requires human intervention (via explicit user request, unresolved intent, or escalation trigger) and seamlessly transfers the chat to a live agent while preserving full conversation history, internal notes, and extracted user data. The handoff mechanism maintains context across the AI-to-human boundary, allowing agents to see what the bot attempted and why it escalated, reducing repetition and improving resolution time.

Routes chatbot responses to multiple communication channels (Slack, Discord, WhatsApp, Facebook Messenger, Instagram, Telegram, Line, Intercom, Crisp) using native API integrations. Each channel integration handles platform-specific formatting (rich cards, buttons, carousels, file attachments) and maintains separate conversation threads. Users can interact with the same chatbot across channels while maintaining independent conversation state per channel.

Automatically detects user message language and translates both incoming messages and outgoing responses in real-time, supporting 100+ languages. Translation occurs transparently within the conversation flow — the chatbot responds in the user's language without explicit language selection. Implementation likely uses a third-party translation API (Google Translate, Azure Translator, or similar) integrated into the message processing pipeline.

Allows users to select from multiple LLM providers and models (GPT-3.5, GPT-4o-mini, GPT-4o, GPT-4 Turbo, GPT-4, o1) on a per-chatbot or per-conversation basis. Each model has a relative cost multiplier (GPT-3.5: 1x, GPT-4o-mini: 0.5x, GPT-4o: 5x, GPT-4 Turbo: 10x, GPT-4: 20x) applied to a monthly AI credit budget. Users can optimize cost vs. quality by selecting cheaper models for simple queries and expensive models for complex reasoning.

Automatically extracts structured lead data (email, name, company, phone, issue type, etc.) from conversation context and routes qualified leads to sales teams. The extraction mechanism likely uses intent detection and entity recognition to identify when a conversation contains lead-qualifying signals (e.g., 'I'm interested in pricing' or 'Can you demo the product?'). Extracted leads can be exported or integrated with CRM systems via Zapier/Pabbly.

Provides dashboards tracking conversation metrics (volume, resolution rate, user satisfaction, response time), user activity patterns (frequency, channels, topics), and engagement metrics. Analytics can be filtered by location, time period, and conversation type. Implementation likely aggregates conversation logs and metadata into a time-series database for visualization and trend analysis.

Provides a 'Flow Logger' tool that records the execution path of each conversation through the chatbot's workflow, showing which steps were executed, what conditions were evaluated, and what data was passed between steps. Developers can inspect failed or unexpected conversations to identify logic errors, missing intents, or data extraction issues without accessing backend logs.

Removes YourGPT branding ('Powered by YourGPT' footer) and allows custom domain configuration (e.g., chat.mycompany.com instead of yourgpt.ai/chat). Widget appearance can be customized (colors, fonts, logo) to match brand identity. Available on Advanced+ tiers, enabling agencies and enterprises to rebrand chatbots as their own products.