Feature Request

Business Problem: Currently, respond.io supports integration with platforms like Zapier and Integromat, but there is no direct integration with n8n.cloud ( n8n.io ). Many businesses prefer n8n due to its lower costs and flexible workflow automation capabilities. Without a native integration, users must rely on workarounds or custom scripts, making automation more complex. Desired Outcome: Enable integration with n8n.cloud ( n8n.io ) to allow seamless automation of workflows. If a full integration is not immediately possible, ensuring that webhook support is available would already help users connect respond.io with n8n efficiently.

Business problem: Currently, AI Agent does not have the capability to process voice notes from messages. This limits platform users from handling situations where contacts prefer speaking rather than typing. Many users rely on voice messages for convenience, cultural reasons, or to describe complex issues faster. Without the ability to process voice notes, agents must manually listen, interpret, and respond, leading to slower resolution times and missed automation opportunities. Desired outcome: Allow AI to process voice notes from incoming messages and generate contextual responses. Specific use cases: Transcribe voice notes into text and provide relevant answers. Understand spoken product queries and suggest matching items. Diagnose issues described verbally and respond based on knowledge sources. Process voice messages containing order details, complaints, or instructions. Use case (Capture, Convert, Retain): This feature spans multiple phases: Capture: Engage users who prefer voice over text, lowering friction and making it easier to interact with the business. Convert: Use voice-based queries for faster product discovery, order placement, and upselling. Retain: Reduce effort for customers by removing the need to type, while ensuring quick and accurate responses that build trust and loyalty.

Business Problem The AI Agent may sometimes send duplicate responses in a conversation. This can confuse customers, make interactions feel less professional, and clutter the chat with repeated messages. Desired Outcome Avoid sending duplicate replies to customers Provide a smoother, more professional experience Reduce unnecessary message volume for both customers and agents

Business Problem: Right now, when the AI Agent responds to a customer and the customer goes quiet, the conversation just stays open. The AI doesn’t follow up, escalate, or close — and there’s no built-in way to handle this idle state. This might lead to: Lost leads if customers drop off Cluttered inboxes with inactive convos Missed chances to act when engagement stalls Desired Outcome: Add smart inactivity handling for AI Agent conversations by enabling the following behaviors: Follow-up Message After Inactivity : If there’s no reply from the customer after a set period (e.g., 3 minutes), the AI Agent should automatically send a follow-up message to re-engage them Conversation Closure After Continued Inactivity : If the customer still doesn’t respond within another predefined duration, the system should automatically close the conversation. Optional Escalation to Human Agent : As an alternative to auto-closing, business should also have the option to assign the conversation to a human agent for review if the customer remains inactive. Use case (Capture, Convert, Retain): Capture : Prevent potential leads from being lost due to inactivity or non-responsiveness Convert : Re-engage prospects to move them further down the funnel Retain : Show existing customers you’re attentive and proactive

Business Problem: Currently, the AI Agent does not have the ability to check whether a user (agent) is online before assigning a contact. As a result, contacts may be routed to offline agents, leading to delays in responses and a poor customer experience. Desired Outcome: Enhance the AI Agent’s Assign to Agent or Assign to Team action so that it verifies agent availability first. The AI should: Detect whether the target agent/team members are online and available. If the chosen agent is offline, automatically assign the contact to another available agent within the team. Optionally, allow businesses to configure fallback behavior (e.g., queue until online, assign to backup team).

As a user, when I am chatting with my customers, I want them to know that I am currently replying to them. For channels that support this (Messenger, WhatsApp, etc.). Note: This FR refers to showing a typing indicator to your customers, meaning your customers will see on their phone when you are typing. If you are referring to internally showing a typing indicator between agents to avoid collision in responding → https://respond.canny.io/feature-request/p/inbox-user-collision-detection-among-users

Business Problem: Currently, there's no way to test an AI Agent before deploying it in a live environment. This lack of a sandbox or preview mode creates friction during setup, increases the risk of misconfigured responses, and slows down iteration. Teams are forced to activate the agent to see how it behaves, which can lead to awkward customer experiences or internal confusion. Desired Outcome: A Test Mode for AI Agents that allows admins to simulate conversations, preview AI behavior, and fine-tune logic — all without affecting live customers. The test interface should reflect how the AI will respond in real scenarios. Use Case (Build + QA) Before Launch : Managers and admins can preview how the AI handles FAQs, product inquiries, or assignment flows. While Tuning : Teams can safely test tweaks to tone, logic, or integrations without needing to go live first. With a Test AI Agent mode, teams can confidently build, experiment, and perfect their AI experiences — without flying blind or breaking things in production 🔧🧪✅

Problem Statement: Currently, our AI features can’t send images or files to customers. This is a major roadblock for both sales and support teams, who often need to share product images, brochures, guides, or troubleshooting documents to give customers the full context or help them solve problems. Without this, customer interactions are less effective and require manual intervention for something as simple as sending a file. Desired Outcome: AI should be able to recognize when an image or file is needed and send the appropriate material automatically, based on the conversation. This would allow customers to get complete answers and resources instantly, boosting satisfaction, speeding up resolutions, and letting teams focus on more complex tasks—without having to jump in just to attach a file.

Business Problem: Currently, users must wait for each knowledge source to finish ingesting before they can upload the next one. This sequential process slows down onboarding and makes managing multiple knowledge sources inefficient, especially for teams handling large volumes of content. Desired Outcome: Enable users to upload and initiate ingestion for multiple knowledge sources at the same time, without having to wait for each to complete. This will streamline the setup process, save time, and make it significantly easier to manage and scale knowledge management within the platform.

Business Problem : Customers are currently unable to increase their AI Credits, and are required to wait until the next billing cycle. Desired Outcome : To enable customers to top up their AI Credits, providing them with the option to increase their credit balance. Running low on AI credits? Upgrade your plan to unlock significantly higher limits and continue using AI worry-free. Head on to https://app.respond.io/organization/billing/plans to explore new plans.