AINiro

Visual drag-and-drop interface for constructing multi-turn dialogue trees with branching logic, variable assignment, and state management. Users define conversation paths without writing code by connecting nodes representing user intents, bot responses, and conditional branches based on user input or external data. The platform compiles these visual workflows into executable conversation logic that handles context across multiple turns.

Native connectors and webhook-based integration layer that enables chatbots to read from and write to external systems including CRMs, ticketing platforms, databases, and custom APIs. The platform provides pre-built integrations for common business tools and a generic HTTP request builder for custom endpoints, allowing conversation flows to fetch customer data, create tickets, update records, and trigger downstream workflows without custom code.

Capability to format bot responses with rich media elements including buttons, cards, images, and links, with formatting adapted to each deployment channel. Users define response templates in the visual builder that include text, structured elements (buttons for actions), and media attachments. The platform automatically adapts formatting for channel constraints (e.g., SMS text-only, web rich formatting) while preserving intent and functionality.

Engine for executing complex conditional logic within conversation flows, including if-then-else branches, loops, and variable-based routing. Users define conditions based on user input, extracted entities, API response data, or conversation context, and the platform evaluates these conditions to determine which conversation path to follow. Conditions support comparison operators, boolean logic, and pattern matching against variables and external data.

State management system that maintains conversation context across multiple user turns, including user-provided information, API response data, and intermediate computation results. The platform stores variables scoped to individual conversations and sessions, allowing later dialogue turns to reference earlier statements, apply conditional logic based on accumulated context, and personalize responses. Context is preserved within a single conversation session and can be passed to integrated backend systems.

NLU engine that maps user inputs to predefined intents and extracts entities from natural language text. The system uses training data (example phrases) provided by users to recognize customer intent and extract relevant information like names, dates, or product references. The platform applies pattern matching and possibly lightweight ML models to classify incoming messages and route them to appropriate conversation branches, though it lacks the sophistication of large language models like GPT-4.

Library of pre-configured conversation templates for common use cases (customer support, sales qualification, appointment booking, FAQ answering) that users can instantiate and customize. Templates include predefined intents, conversation flows, and integration points that accelerate initial setup. Users can clone a template, modify the conversation logic and integrations to match their specific needs, and deploy without building from scratch.

Capability to deploy the same chatbot logic across multiple communication channels (web chat widget, messaging apps, email, SMS) with channel-specific formatting and behavior. The platform abstracts conversation logic from channel implementation, allowing a single workflow to handle conversations regardless of input channel. Messages are normalized on input and formatted appropriately on output for each channel's constraints and conventions.

Dashboard and reporting system that tracks chatbot performance metrics including conversation volume, intent recognition accuracy, user satisfaction, conversation completion rates, and integration success rates. The platform logs all conversations and provides filtering, search, and export capabilities for analysis. Metrics are aggregated at conversation, intent, and time-period levels to identify bottlenecks and improvement opportunities.

System for identifying and authenticating users within conversations, including support for anonymous sessions, email/password authentication, and integration with external identity providers (OAuth, SAML). The platform maintains user profiles and conversation history linked to authenticated identities, enabling personalization and context continuity across sessions. Authentication can be enforced at conversation start or triggered conditionally during the flow.

Escalation mechanism that transfers conversations from chatbot to human agents when the bot cannot resolve an issue or when the user explicitly requests human assistance. The platform maintains conversation context during handoff, allowing agents to see the full conversation history and any collected data. Handoff can be triggered by explicit user request, failed intent recognition, or predefined escalation rules based on conversation content or duration.

Capability to export conversation transcripts, user data, and interaction logs in standard formats (CSV, JSON) for compliance, analysis, and archival purposes. The platform supports filtering exports by date range, user, intent, or other criteria. Exports include full conversation history, extracted data, API call results, and metadata. The system maintains audit trails of data access and export operations for compliance monitoring.