Feature Request

Business Problem Customers are currently unable to increase their AI Credits once they reach their limit, forcing them to wait until the next billing cycle. This creates a significant disruption in critical business work flows, such as automated customer support and lead qualification, which rely on uninterrupted AI availability. Desired Outcome To enable customers to top up their AI Credits both manually and or automatically (e.g. similar to MAC overcharges).

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: 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: Right now, Respond.io ’s AI Agent doesn’t have “conversation event” context—meaning it isn’t aware of important state changes like a conversation being closed and then reopened by a new inbound message. Because of this, the AI Agent often behaves as if nothing significant changed, even when the platform clearly shows the conversation has re-entered an open state. A common scenario looks like this: An issue is resolved and the conversation is closed. Shortly after, the contact sends a final acknowledgement like “Ok thank you” or “👍”, which reopens the conversation. The message does not indicate a new request; it’s simply a closing remark. The AI Agent should recognize this as a reopen caused by a polite follow-up and re-close the conversation automatically. Instead, the conversation stays open unnecessarily, which creates inbox clutter, adds extra triage work for human agents, and introduces noise in reporting (reopen counts, resolution time, and backlog). There’s a second related behavior: when the AI Agent has already called a tool earlier in the conversation, it tends to avoid calling that same tool again later—even when it’s perfectly valid and necessary because the conversation state has changed (for example, after a reopen event). This “one-and-done” tool behavior reduces reliability in event-driven flows. Desired Outcome: AI Agent should be able to understand and use conversation events—especially closed → reopened transitions—so it can make state-aware decisions. When a conversation is reopened and the new inbound message is clearly an acknowledgement with no new intent, AI should confidently re-close the conversation to keep the inbox clean and prevent unnecessary follow-ups. In addition, conversation events should act as a strong signal that prior actions may need to be repeated. AI Agent should be allowed (and encouraged) to call tools again when an event indicates a new state, rather than treating a previously-called tool as “off-limits” for the rest of the thread. This helps the Agent behave more like a dependable operator: aware of what just happened, and comfortable repeating the correct action when conditions warrant it.

Business Problem: Currently, the HubSpot integration with respond.io only supports the Contact object, limiting the ability to track and manage sales opportunities. Businesses using HubSpot Deals to monitor their sales pipeline lack visibility within respond.io , leading to fragmented sales data, as deal-related insights are only available in HubSpot. Desired Outcome: Support for HubSpot Deals Object: Enable users to sync, view, and update HubSpot Deals within respond.io . Use Cases: Sales Follow-Ups: A sales rep chatting with a prospect on WhatsApp can instantly check their associated deal and update the deal stage in respond.io .

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: 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.

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, contact updates made in HubSpot are not reflected automatically in respond.io . This leads to data inconsistencies, outdated contact attributes, and operational inefficiencies when agents rely on inaccurate CRM information. Since HubSpot is typically the source of truth for sales data, the lack of downstream sync creates misalignment between sales and conversational teams. Desired Outcome: Enable one-way contact sync from HubSpot to respond.io , where updates made in HubSpot automatically reflect in respond.io . This should allow users to configure: • Field mappings between HubSpot and respond.io • Sync scope (create only | update only | create & update) Current Workaround: At present, users must: Manually update contact details in both respond.io and HubSpot. Use third-party automation tools like Zapier or Make.com to sync