Feature Request

Business problem: Currently, AI Agent does not have the capability to process images from messages. This feature would help platform users handle objective queries where the contact sends an image, AI Agent will not be able to process it answer based on the image that the contact is enquiring about. Desired outcome: Allow AI to process images based on the image from messages. Specific use cases: Identify brands of items of the image and provide a response on said brand / product Identify issues from images and provide a response based on knowledge sources (i.e. image of a malfunctioning device of a specific brand) Process an image of a form, receipt, etc Use case (Capture, Convert, Retain): This feature spans multiple phases: Capture: Visually engage users early in the journey—think visual search or product discovery via images. Convert: Streamline image-based product matching and upselling in chat. Retain: Use visual input to reduce back-and-forth and solve problems faster.

Business problem: As an agent on the go, I want to handle support Channel calls such as WhatsApp, Messenger, etc. directly from the mobile app, so that I can provide seamless customer service without being tied to a desktop. Desired outcome: Allow agents to initiate and receive calls via the mobile app. Use Cases: Agents resolving customer issues while traveling. Field agents managing customer calls during site visits. The current workaround: N/A

Business Problem: Our platform currently lacks a personalized product recommendation feature. This absence limits our ability to guide customers towards products that best suit their needs and preferences, potentially leading to missed sales opportunities and reduced customer satisfaction. Desired Outcome: Implement a new AI Agent objective that suggest relevant products based on Contact's conversation. This objective should improve user experience by offering tailored suggestions, increasing the likelihood of purchases, enhancing customer engagement and able to capturing intent after recommending products. Use case (Convert): This feature clearly shines in the Convert phase. You're already engaging the customer—they’ve reached out or responded—and now it's time to seal the deal. The AI doesn’t just inform; it persuades. It uses the context of the chat to: Recognize buying signals Suggest products that align with the customer’s use case Help the customer make a confident, informed buying decision So, you're not just solving for engagement—you’re solving for revenue.

Business Problem: Teams cannot easily identify patterns, bottlenecks, or high-performing stages, limiting their ability to optimize the customer journey. The absence of advanced tools like a Sankey diagrams or conversion matrices means businesses struggle to gain a comprehensive view of lifecycle dynamics, impacting their ability to improve conversion rates and reduce drop-offs. Desired Outcome: Introduce advanced lifecycle analysis reports with the following features: Sankey Diagrams: Visualize customer flow across lifecycle stages and highlight where drop-offs occur or identify stages with the highest retention. Conversion Matrix Table: Display stage-to-stage transition rates in a matrix format that provide clear metrics on movement between specific stages (e.g., Lead → Opportunity → Customer). These reports can include metrics such as: Stage Conversion Rates: How effectively contacts move between stages. Drop-Off Rates: The percentage of contacts lost at each stage. Average Time Spent per Stage: Insights into delays in the pipeline. Stage Re-Entry Rates: Track customers who re-enter a previous stage. Lifecycle Trend Analysis: Track historical lifecycle data to identify seasonal trends or long-term changes in customer behavior.

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.

It helps the agents automatically close the conversation after 30 minutes duration. It enables the agent to save extra time a lot.

Business Problem: Right now, Respond AI Agents can't interact with external systems in real time — limiting their ability to trigger workflows, fetch dynamic data, or complete transactions. Without the ability to make HTTP calls, the AI is cut off from countless integrations and use cases that require external API access, forcing handoffs to humans or third-party automations. Desired Outcome: An AI Agent capability to make HTTP requests (GET, POST, PUT, DELETE) to external endpoints. This would allow AI Agents to fetch live data, update CRM records, trigger automations, check order statuses, submit forms, or interact with custom services based on the conversation flow. This unlocks endless possibilities for customization, automation, and AI-driven execution. Use Case (All Lifecycle Phases) * Capture : Call a lead enrichment API to personalize outreach. * Convert : Check shipping costs or create orders via third-party services. * Retain : Fetch ticket status or update records in real-time. With HTTP support, Respond AI transforms from a chat assistant into an action engine — fully capable of reading, writing, and reacting across your ecosystem in real time ⚙️🌐🚀

Business Problem: Currently, contact information between respond.io and Salesforce does not sync bidirectionally. Changes made in one platform do not automatically reflect in the other, leading to: Data inconsistency, where outdated or mismatched contact details exist across platforms Desired Outcome: Configurable Contact Sync: Allow users to define how contact data syncs between respond.io and Salesforce: One-way sync (Salesforce → respond.io ): Changes made in Salesforce are reflected in respond.io One-way sync ( respond.io → Salesforce): Updates in respond.io are pushed to Salesforce Two-way sync: Changes made in either system update the other automatically. Use Cases: Seamless Updates: A sales rep updates a contact's lead status in Salesforce, and the change is instantly reflected in respond.io . Current Workaround: At present, users must: Manually update contact details in both respond.io and Salesforce. Use third-party automation tools like Zapier or Make.com to sync

Business Problem: Currently, Respond AI only supports text data as input for its knowledge source. This limits its ability to generate accurate and helpful responses in scenarios where essential information is provided in image formats—such as product photos, or visual instructions. Without image support, our AI Assist and AI Agent features cannot reference or reason over visual content, leading to incomplete responses and reduced automation potential in image-heavy workflows. Desired Outcome: Enable Respond AI to process and utilize image data uploaded to the knowledge source. The system should be able to extract and index relevant information from images and make that information accessible to both AI Assist and AI Agent for generating contextually rich responses. This enhancement will extend Respond AI’s capabilities beyond text and support more use cases.

Business problem: Right now, users can define their own Knowledge Sources to be used for AI Agent and AI Assist. The problem is that, the Knowledge Source must be textual in order for AI to understand it properly. If the Knowledge Source has tables, AI cannot process the table in that Knowledge Source. Desired outcome: Allow Respond AI to understand and interpret data tables. This can be done by: Defining a table in PDF's Uploading a CSV to Knowledge Sources This would help users configure AI to provide data or answers based on these data tables defined. Use case (Convert): This feature is designed for the Convert phase of the sales cycle, allowing users to upload a simple product availability table—typically with key details like name, price, and product information—so AI can instantly understand what's available and recommend the right products to customers.