Feature Request

Problem Statement: Agents handling appointments or demos over chat have no way to create or update a Google Calendar event from within Respond.io . They have to switch over to Google Calendar to book or edit a meeting, then come back to the conversation to relay it to the contact — with no guarantee the two stay in sync afterward. Desired Outcome: Let agents create and edit Google Calendar events directly inside the conversation, so scheduling stays in sync with what's discussed in chat and doesn't require jumping between tools.

Business problem: As a user, I would like to automatically send a public reply from respond.io to contacts who comment on my Instagram posts or Reels, so that I can capture and engage leads while their interest is at its peak. Desired outcome: Auto-reply to comments publicly Boost engagement and conversion in real time Use Cases: A user comments on a Reel and receives an public reply A follower asks a question and gets a public + private response The current workaround: N/A

Problem Statement: When a contact wants to book or reschedule a meeting during a conversation, agents can only send a Calendly link and ask the contact to do it themselves — Respond.io has no way to create or edit a Calendly event directly. Every booking or change happens outside the chat, so agents lose visibility into what was actually scheduled and have to switch tools to confirm it. Desired Outcome: Let agents create and edit Calendly events directly from within a conversation, so meetings can be booked, rescheduled, or updated without leaving the chat. Use Cases: An agent on a live chat agrees a new time with a customer and books the appointment on the spot, inside the conversation, without copy-pasting a booking link. A customer messages to reschedule; the agent moves the meeting to a new slot directly from the inbox and the customer receives the updated calendar invite. A customer cancels last minute; the agent cancels the booking in respond.io , freeing the slot in Calendly immediately. An agent adds internal notes or updates attendee details on an existing booking while reviewing the conversation context.

Business problem: AI Agent currently can crawl Knowledge Sources defined for AI Agent. However, it's hard for platform users to identify which part or snippet of the Knowledge Source that the AI Agent is referencing. Without a log to tell the user what Knowledge Source it is referencing, platform users can not identify clearly which part of the Knowledge Source they should improve or change so that the AI Agent can provide more accurate answers Desired outcome: When an AI Agent replies, there should be a log to tell the platform user what part of the Knowledge Source is being crawled. This can be an activity log or an information call-out for users to refer with a "snippet" or source of the Knowledge Source

Business problem: Customers don't know how their agents are using AI features Desired outcome: It would be good if customers were able to view how users are using AI features e.g AI Assist Example of statistics: Which user used the AI features Changes in messages made by the AI vs original human message

Business Problem: Currently, businesses running TikTok ads must leave respond.io and open TikTok Ads Manager to see how their ads are doing. Ads Manager also stops at the click. It cannot show what the people from each ad became, because those conversations and outcomes sit in respond.io . Desired Outcome: Connect a TikTok ad account and ask AI Copilot simple questions, like "How are my TikTok ads doing this week?" Copilot answers with spend, clicks and cost per result, and shows rejected ads with the reason. Copilot also answers questions that mix both sides, by linking each contact to the ad they came from. The business chooses what Copilot may do, and can keep it view-only. Use Cases: A marketer asks how the contacts from one ad feel, and gets their sentiment in one answer. A business owner asks which ads brought paying customers, and what each one cost. A marketer learns on the same day that an ad was rejected, and why. Current Workaround: Group Lifecycle Reports by ad campaign, ad group or ad to see the contacts from an ad, then open TikTok Ads Manager for spend and ad status, and compare the two by hand.

Business Problem: Currently, businesses running Click-to-WhatsApp, Messenger or Instagram ads must leave respond.io and open Meta Ads Manager to see how their ads are doing. Ads Manager also stops at the click. It cannot show what the people from each ad became, because those conversations and outcomes sit in respond.io . Desired Outcome: Connect a Meta ad account and ask AI Copilot simple questions, like "How are my Meta ads doing this week?" Copilot answers with spend, clicks and cost per result, and shows rejected ads with the reason. Copilot also answers questions that mix both sides, by linking each contact to the ad they came from. The business chooses what Copilot may do, and can keep it view-only. Use Cases: A marketer asks how the contacts from one ad feel, and gets their sentiment in one answer. A business owner asks which ads brought paying customers, and what each one cost. A marketer learns on the same day that an ad was rejected, and why. Current Workaround: Group Lifecycle Reports by ad campaign, ad group or ad to see the contacts from an ad, then open Meta Ads Manager for spend and ad status, and compare the two by hand.

Business problem As an agent handling customer conversations, I want to create, reschedule, and cancel Cal.com meetings directly inside respond.io , so that I don't have to leave the conversation and switch to Cal.com to manage bookings while I'm talking to a customer. Desired outcome A feature that lets agents create a new booking, edit an existing booking (time, date, attendee, notes), and cancel a booking from within the respond.io — without opening Cal.com separately. Changes sync back to Cal.com and trigger the customer's normal confirmation/reschedule notifications. Use Cases An agent on a live chat agrees a new time with a customer and books the appointment on the spot, inside the conversation, without copy-pasting a booking link. A customer messages to reschedule; the agent moves the meeting to a new slot directly from the inbox and the customer receives the updated calendar invite. A customer cancels last minute; the agent cancels the booking in respond.io , freeing the slot in Cal.com immediately. An agent adds internal notes or updates attendee details on an existing booking while reviewing the conversation context. The current workaround Currently, agents can only share a Cal.com booking link and let the customer self-serve, or they leave respond.io , log into Cal.com , find the booking, and make the change manually — then return to the conversation to confirm with the customer.

Business Problem: Managers currently have no scalable way to assess conversation quality across AI Agent and human agent interactions — they're manually reading through closed conversations to spot problems. As AI Agent volume grows, this doesn't scale. The only in-platform option today is the CSAT workflow template, which captures customer satisfaction but doesn't tell managers what went wrong (e.g. was the issue actually resolved, was the handover smooth, did the agent follow the right process). The alternative is stitching together an n8n + AI workaround, which requires developer setup and isn't accessible to most teams. A single sentiment score (positive/negative/neutral) on its own also wouldn't be enough for managers who need to evaluate conversations against criteria specific to their own process. Desired Outcome: Let admins/managers define their own scoring criteria (beyond just sentiment) that AI applies automatically to every closed conversation — for example, issue resolved, handover smoothness, process adherence, tone appropriateness. Scores would surface in reporting so managers can filter, flag, and drill into conversations that fall below a threshold, without reading every transcript manually.

Business problem respond.io already supports MCP, but reporting and analytics data are not currently available through it. This limits teams that want to pull operational and performance data from respond.io into external AI assistants, internal tools, dashboards, or workflows. Without reporting and analytics access via MCP, users must check reports manually in the platform or rely on separate exports and workarounds. This creates friction for teams that want faster analysis, cross-system visibility, and more automated decision-making. Use cases Let teams query conversation, agent, channel, and lifecycle performance data from external AI tools or internal assistants Allow businesses to combine respond.io reporting data with CRM, BI, or warehouse data for broader analysis Help managers automate recurring reporting workflows instead of manually checking dashboards Desired outcome Make reporting and analytics data accessible through MCP