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Feature: Button Groups for Workflow Nodes

Background & Problem

Currently, workflow nodes do not support rendering button groups in the chat interface. This limits our ability to guide users through conversations in a clear and intuitive way.
In addition, the system only supports link buttons, which restricts interactive use cases such as conversational branching or quick-reply suggestions.

Proposal

Introduce a dedicated Button Group feature that allows creators to define and display a list of buttons within a workflow node.
A button group can contain multiple buttons, each with a defined behavior. This enables richer interaction patterns and clearer user guidance within business workflows.

Button Types

Each button in a button group can be one of the following three types:
  1. Link Button
  • Matches the current behavior.
  • Configuration includes a button label and a URL.
  • Clicking the button redirects the user to the specified URL.
  1. Inline Suggestion Message Button
  • Behaves like existing suggestion message chips.
  • Clicking the button sends a predefined message on behalf of the user.
  • The message is treated as if the user manually entered it.
  1. Logic Transition Button
  • Used for conversational flow control.
  • Configuration includes a button label and a connection to a target workflow block.
  • Clicking the button triggers a transition to the connected block node, advancing the conversation state.

Expected Impact

By combining these three button types with existing workflow block nodes, we can support the full range of button-based interaction patterns required in business workflows, including:
  • Guided user navigation
  • Quick replies and intent selection
  • Conditional conversation branching
This feature significantly improves usability, flexibility, and clarity in chat-driven workflows.

Next

Feature: MCP Integration for AI Conversation Nodes

Background & Problem

Currently, MCP (Model Capability Plugin) functionality is only available within AI Action nodes, and its execution is controlled implicitly by the base prompt.
The AI Conversation node does not support MCP capabilities, which limits control over:
  • When MCP skills are triggered
  • How MCP outputs are used across complex workflows
This makes it difficult to precisely orchestrate MCP usage in business workflows that involve branching logic or downstream processing.

Proposal

Add MCP integration directly to the AI Conversation workflow node.
By embedding MCP capabilities into the node itself, workflow creators gain explicit and fine-grained control over:
  • When a specific MCP skill is invoked
  • How MCP outputs are consumed by downstream nodes
  • How MCP execution aligns with complex workflow logic

Benefits

  • Precise MCP triggering: MCP skills can be executed at deterministic points in the conversation flow rather than relying on prompt-based heuristics.
  • Improved workflow composition: MCP outputs can be explicitly referenced and reused in downstream nodes.
  • Greater reliability: Reduces ambiguity and unintended MCP invocations caused by prompt interpretation.
  • Enhanced expressiveness: Enables more sophisticated business workflows that combine conversation, logic, and MCP execution.

Rationale

The existing AI Conversation node is the most suitable place to add MCP capabilities, as it already serves as the primary interface for conversational logic and user interaction within workflows.
Integrating MCP here aligns functionality with user intent and significantly amplifies the precision and effectiveness of MCP usage in end-to-end business workflows.

Later

Feature: Event-Driven MCP Triggers for AI Agents

Background & Problem

We currently integrate with 833 applications through MCP integrations. However, these integrations are limited to on-demand usage and cannot react to events emitted by the connected applications.
As a result, our AI agents are primarily constrained to handling user-initiated messages, rather than responding automatically to real-world system events.

Proposal

Introduce event-driven MCP triggers that allow AI agents to listen to and respond to specific events from MCP-integrated applications.
With this capability, AI agents can automatically handle workflows such as:
  • Listening to incoming emails (e.g., Gmail) and generating replies
  • Responding to new or updated CRM tickets
  • Acting on events from third-party systems without manual user input

Key Capabilities

  • Subscribe to specific events exposed by MCP integrations
  • Automatically trigger AI agent workflows when events occur
  • Use AI-generated responses to take action within the originating system

Benefits

  • Fully automated workflows: AI agents can respond to events without requiring API calls or manual triggers.
  • Expanded platform scope: Moves the platform beyond chat-based interactions into system-driven automation.
  • Limitless use cases: Enables continuous, event-based intelligence across email, CRM, support, and other business systems.
  • Lower integration friction: Users do not need to write or maintain custom APIs to trigger AI responses.

Impact

By enabling event-driven triggers for MCP integrations, our platform becomes a proactive automation layer rather than a reactive chat interface. This significantly amplifies the reach and value of our AI agent capabilities across business workflows.

Add the Phone call capability

Add the Phone call capability to the AI Agent, like retellAI and Vapi

Audo/Video live chat

The chatbot agent or admin can initiate the audio//video live chat directly in the chatbot, avoiding back and forth typing.

Build the I18n of the SSO platform interface

Currently, the SSO platform only supports English. We should be able to change the language to the selected locale of the company

Feature: Transform Analytics into a Business Intelligence Platform

Background & User Feedback

Users have expressed that the current Analytics section does not meet expectations for understanding the real business impact of their chatbot.
While basic metrics are available, the analytics experience lacks actionable insights that help business owners evaluate performance, identify opportunities, and optimize operations.

Problem Statement

The existing analytics offering:
  • Does not provide sufficient visibility into user interactions and outcomes
  • Falls short of serving as a decision-making tool for business users
  • Does not differentiate value for higher-tier customers (e.g., SSO and enterprise plans)

Proposal

Evolve the Analytics section into a Business Intelligence (BI)–style experience powered by chatbot interaction data.
This enhancement will provide both standardized insights and advanced, customizable analytics tailored to business needs.

Core Metrics to Add

Include easily accessible, interaction-based metrics such as:
  • Most common user questions and intents
  • E-commerce purchase tracking driven by chatbot interactions
  • Most requested or viewed products
  • Accepted calls to action (CTA conversion rates)

Advanced Analytics (Premium / Enterprise)

For users on SSO and top-tier plans, introduce deeper analytical capabilities, including:
  • Multi-dimensional interaction analysis (by user, channel, time, or segment)
  • Funnel and conversion analysis across chatbot-driven journeys
  • Trend analysis to identify behavior changes over time

Conversational Analytics (Future Enhancement)

Introduce a conversational AI interface for analytics that allows users to:
  • Query chatbot performance using natural language
  • Generate custom reports on demand
  • Explore data dynamically without manual dashboard configuration

Expected Impact

  • Actionable insights: Helps businesses understand how the chatbot contributes to outcomes, not just activity.
  • Operational optimization: Enables data-driven improvements across sales, support, and engagement workflows.
  • Plan differentiation: Creates clear added value for premium and enterprise customers.
  • Increased adoption: Positions analytics as a core product capability rather than a passive reporting feature.

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