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    3157 Ideas

    Peter SchöllNew Participant

    Load Messenger before cookie consent with contextual cookie promptSubmitted

    Feature request: Load Messenger before cookie consent with contextual cookie promptCurrent behavior: The Intercom Messenger is currently only loaded after a visitor has given cookie consent via a cookie banner (e.g., OneTrust, Cookiebot, UserCentrics).If a visitor declines or dismisses the cookie banner, the Messenger does not load at all.The problem: This creates a poor user experience:Visitors who decline cookies have no way to contact support or ask questions There is no visible support channel on the page Users may not realize that accepting cookies would unlock the support optionFrom a business perspective, this means:Lost support inquiries Reduced engagement No alternative communication path for privacy-conscious usersProposed solution: Allow the Messenger to load in a "cookie-pending" mode before consent is given.In this mode:The Messenger widget is visible and can be opened When a user tries to interact (e.g., start a chat), the Messenger displays a contextual message explaining that cookie consent is required A button is shown that re-opens the cookie consent dialogThis approach:Maintains visibility of the support channel Educates users on why consent is needed Provides a clear path to grant consent in context Complies with GDPR (no cookies set before consent)Technical note: The Messenger could load in a "consent-required" state and trigger the cookie banner via standard IDs or API calls, for example:OneTrust: <button id="ot-sdk-btn">Manage cookie settings</button> UserCentrics: trigger via their JavaScript API or custom elementThis would work seamlessly with common consent management platforms.How this would support our workflow:Visitors always see a support option, regardless of their initial cookie decision We can offer a consent-in-context flow that's clearer and more user-friendly Support remains accessible for privacy-conscious users who may reconsider after understanding the value Reduces friction in the customer journeyReferences: This pattern is already used by some modern support tools and is considered a best practice for GDPR-compliant UX.

    Roy
    Top Expert ✨
    RoyTop Expert ✨

    🚀 Feature Request: Evolution of Fin — From "Answer Engine" to "Knowledge Architect"Submitted

    The end of the static Knowledge Base is here Currently, Fin is the best in the business at retrieving answers. But even the best librarian is limited by the books on the shelf. To win the AI race, Fin needs to stop just reading the library and start building it. 🏗️ The Vision: The "Self-Healing" Support Ecosystem 🧠 Right now, the Knowledge Base (KB) is a bottleneck. It’s manually written, often out of date, and disconnected from the "grit" of real-time customer struggles. The Future: Fin shouldn't just be an "Answer Engine"—it should be a "Knowledge Architect." 🏛️ How it Works (The Unlocked Power): Real-Time Knowledge Mining 💎: Fin should analyze every live conversation. When it identifies a successful resolution to a problem not in the KB, it should flag it instantly.   Autonomous Article Drafting ✍️: Instead of a Support Manager spending hours writing docs, Fin should "draft" a new KB article based on the transcript of a perfectly handled chat or update existing.   Semantic Refresh 🔄: If customers start using new slang or technical terms for an old problem, Fin should update the tone and keywords of existing articles in real-time to match the actual language of the user.   Gap Identification 🔍: Fin should proactively tell us: "I’ve seen 15 people ask about [X] in the last hour; we have zero documentation on this. Should I create a draft?"   Why This is the Intercom Moat 🏰  Velocity Beats All ⚡: Competitors are still asking humans to manually update "Help Centers." If Intercom can offer a Self-Optimizing Knowledge Base, it will reduce the "Human-in-the-loop" requirement to a simple "Approve" button.   Accuracy at Scale 📈: By using Conversations as the Source of Truth, Intercom should ensure that Fin is never hallucinating based on 6-month-old data. It learns from what worked five minutes ago.   The Growth Play 🚀: This transforms Intercom from a "support tool" into a Product Intelligence Engine. Intercom not just resolving tickets it’s mapping the customer’s pain in real-time.   The "Mic Drop" Moment 🎤💥 By turning Fin into a Knowledge Architect, Intercom creates a platform that literally gets smarter with every single message sent. Let’s stop answering questions and start building the future of collective intelligence. ✅BTW: I’m aware Intercom has Content Gap Recommendations, but currently, that feature is a patch for the bot. It identifies where Fin failed so you can feed it a snippet to stop the escalation. My vision for a Knowledge Architect is different: it’s not about 'fixing Fin'; it’s about using the 'grit' of live conversations to automatically evolve the Help Center articles themselves. Intercom just trains an AI Agent; It should be using AI ensure that public knowledge is a real-time reflection of the product, written in the customer’s actual language. Stop Hiding Knowledge Gaps in the "Reports" Basement. 🏢📉Currently, most AI "insights" or "content gaps" are buried in an Analytics dashboard. A true Knowledge Architect lives when and where the work happens.Cheers,Roy 🇬🇧

    Roy
    Top Expert ✨
    RoyTop Expert ✨

    Feature Request: ✨ AI-Powered Semantic Prioritization & Smart SortingSubmitted

    Hi Intercom Team 👋🇬🇧, ℹ️ Feature Request: ✨ AI-Powered Semantic Prioritization & Smart Sorting 🤯 The Problem: Chronological BottlenecksCurrently, conversation sorting is limited to chronological or rigid SLA-based metrics (e.g., "Last activity" or "Next SLA"). In a high-volume environment with 30-40 open chats, this "dumb" sorting treats a simple "Thank you" with the same urgency as a "System Down" emergency. Agents waste significant mental energy manually triaging lists to find critical issues, leading to burnout and delayed responses for high-value customers. ✍️ The Solution: Semantic AI SortingWe propose adding a ✨ AI Priority sorting option to the conversation management dropdown. This feature would utilize a "Middleware Triage" to analyze the intent and sentiment of every incoming message in real-time. 😇 Dynamic Triage: The AI automatically identifies and bubbles up frustrated users or high-intent leads (e.g., "Payment failed" vs. "Just checking in").✅ Seamless Integration: A new "AI Recommended" sort option should be placed at the top of the existing sorting menu. 🍳 Low-Level Effort: This can be implemented using existing LLM APIs (like Gemini) to return simple priority tags (e.g., priority: high) without restructuring the entire database. 👔 Why This Matters for the Product Efficiency: Agents move from "First-In-First-Out" to "Value-First" processing, ensuring the most impactful tasks get immediate attention. Customer Retention: High-urgency tickets are resolved faster, directly reducing churn risk for frustrated users. 🏦 Scalability: As chat volume spikes, AI sorting acts as an automated supervisor, preventing critical messages from being buried in the "Documentation Black Hole. Cheers,Roy 🇬🇧