Feature Request

Business Problem: When a business connects their WhatsApp Business App (WABA) to the WhatsApp Cloud API, existing conversations are not imported into the new environment. This creates a disjointed experience for users who expect to see their past chats, leading to confusion, loss of context, and operational inefficiencies for support and sales teams. Desired Outcome: Allow import of existing conversation history from the WhatsApp Business App when transitioning to the Cloud API, allowing users to continue conversations seamlessly without losing prior message context.

Add Googles RCS Messaging as a channel integration.

Business Problem: Agencies and advanced users increasingly use AI assistants (Claude, Claude Code, ChatGPT, Cursor) to build, configure, and maintain customer workspaces. Today this creates two distinct blockers: For everything the REST API covers (contacts, messages, custom fields, broadcasts), users must write custom scripts every time instead of asking the assistant directly. For everything the API does not cover (Workflows, AI Agents, Snippets, channel configuration), the work is 100% manual in the UI — no AI assistance, auditing, or automation is possible at all. For agencies managing dozens of client workspaces, this multiplies onboarding time and increases configuration errors. Desired Outcome: An official respond.io MCP (Model Context Protocol) server that allows any compatible AI assistant to connect via a standard connector and execute actions in natural language without writing code. Contact management: Search, update, tag, and move contacts across lifecycle stages via natural language. Messaging and broadcasts: Send broadcasts to tagged segments, read and summarize unanswered conversations. Snippets: Create, list, and detect duplicate or outdated snippets programmatically. Audit and diagnosis (stretch goal): Read and inspect Workflow and AI Agent configurations to assist with setup, auditing, and cross-workspace comparison. Minimum actions the MCP should expose: Read / create / update contacts, custom fields, tags, and lifecycle stages Send individual messages and broadcasts Read conversation history and internal comments Programmatically create, list, and edit Snippets (Stretch goal) Read and inspect Workflow and AI Agent configuration for auditing and setup assistance

Business Problem: Currently, respond.io only integrates with the Salesforce Contact object and lacks visibility into related Opportunities, which represent deals in progress. For sales teams using Salesforce as their CRM, not being able to view or reference Opportunities directly from respond.io makes it difficult to personalize conversations or track deal progress during real-time interactions. As a result, sales reps must switch between platforms to access deal information, leading to workflow fragmentation, slower responses, and missed opportunities for timely follow-ups. Desired Outcome: Enable support for the Salesforce Opportunity object within respond.io , allowing users to: View opportunities associated with a contact directly from respond.io Use Cases: A sales rep chatting with a prospect can reference the current deal stage or expected close date without leaving respond.io

Business problem: As a user, I want IVR and call queuing capabilities for calls so that I can efficiently manage and direct incoming customer inquiries, reducing wait times and improving customer satisfaction. Desired outcome: Interactive Voice Response (IVR) for automated call routing. Call queuing system with customizable hold messages. Reporting on call queue metrics. Use Cases: Directing customers to the appropriate department automatically. Managing high call volumes effectively. The current workaround: N/A

Business Problem: AI Agents can perform follow-up actions when instructed, but users have no clear way to see when a follow-up has been scheduled or executed. The only current method is to manually watch the conversation and infer the action from the messages, which is unreliable and time-consuming. This lack of visibility makes it harder for users to audit the AI Agent’s behavior and trust its automation. Desired Outcome: Provide a clear, visible indicator that shows when an AI Agent has scheduled or carries out a follow-up action, giving users immediate clarity and confidence during verification or review.

Business Problem The current Voice AI Agent does not support several native dialects and regional accents. For businesses operating in specific markets, using a non-native or mismatched accent (e.g. Latin American Spanish instead of Castilian Spanish, or non-native Portuguese instead of Brazilian Portuguese) can negatively impact: Customer trust Perceived professionalism Cultural accuracy Overall call experience In some cases, existing voices may sound unnatural or incorrectly accented for the target region Desired Outcome Provide native dialect / regional accent options within Voice AI Agent to better support localised customer experiences. Examples of accents not currently available: Spanish (Spain - Castilian) Portuguese (Brazil - native Brazilian accent) This would allow businesses to select a voice that accurately reflects their target market’s pronunciation, tone, and cultural expectations

Business Problem: Digital marketing teams running Google Ads face challenges connecting ad performance directly to messaging outcomes. While Google Ads supports Click-to-WhatsApp CTAs, attribution is unreliable because WhatsApp does not allow referrer tracking. Without proper attribution, businesses cannot measure ROI accurately, optimize campaigns, or connect Google Ads conversions to lifecycle reporting. Desired Outcome: Provide clear source and campaign attribution for leads entering via Google Ads Click-to-WhatsApp campaigns. Surface Google Ads campaign performance in lifecycle reporting alongside other marketing channels. Use Cases: A retail business runs Google Ads campaigns with Click-to-WhatsApp buttons. Each incoming lead is automatically tagged with campaign source for accurate ROI measurement. A company wants to optimize ad spend by sending conversion goals (e.g., qualified leads, closed deals) back to Google Ads for smarter bidding. Marketing teams can view Google Ads contribution directly in lifecycle reports

Business Problem: Currently, if a contact exists in respond.io but not in HubSpot, there is no automated mechanism to create the new contact in HubSpot. This results in fragmented customer data, manual duplication efforts, and the risk of misalignment between platforms. Desired Outcome: Automated One-Way Sync: When a contact is found on respond.io but not HubSpot, the system should automatically create a corresponding contact in the missing platform. Real-Time Data Alignment: Ensure that both platforms remain continuously and accurately synchronized, providing teams with up-to-date contact records wherever they work. Use Cases: Unified Sales & Support Operations: A business interacts with a new customer on WhatsApp, Messenger, or Instagram via respond.io . If the contact doesn’t exist in HubSpot, it is automatically created for sales follow-ups. Current Workaround: At present, you must rely on third-party integration platforms like Zapier or Make.com to detect when a contact exists in one system but not the other and then create it accordingly.

Business Problem: Currently, there is no direct integration between respond.io and HubSpot to automatically capture and log all respond.io activities on the corresponding HubSpot contact’s timeline. Without this centralized, automated recording of key interaction details, users must manually transfer information—leading to data discrepancies and communication gaps. Desired Outcome: Seamless Activity Sync: Automatically log all respond.io activities (messages and calls) to the HubSpot contact record. Comprehensive Interaction Details: Include critical data points such as the date and time of messages, when conversations were opened or closed, who opened or closed them, who assigned them to team members, any conversation summaries or categories, call durations and more. Use Cases: Sales Handoffs: A sales representative can review a HubSpot contact record and instantly see all recent respond.io conversations (messages and calls), including timestamps, participants, and conversation summaries, before making a follow-up call or sending a proposal. Marketing Insight: Marketers can analyze the automatically logged activity data in HubSpot to identify communication patterns, conversation categories, and engagement trends. This insight informs campaign targeting, messaging strategies, and overall customer journey improvements. Operational Efficiency: Managers can track conversation handling times, assignment patterns, and resolution notes directly in HubSpot, reducing the need to switch platforms or search multiple systems for complete customer interaction histories. Current Workaround: At present, users rely on third-party integration tools such as Zapier or Make.com to sync activity data into HubSpot.