AI Feedback Platforms

The Paradox of Growth: The Bigger You Get, the Less You Know I came across something that stuck with me: When companies scale, they gain users — but lose understanding. Not because they stop caring, but because their customer feedback starts living everywhere — support tickets, sales calls, forums, surveys, social media, and app store reviews. That thought really made me pause. I’ve seen this firsthand. When a company is small, every piece of feedback feels personal — every bug report or review has a face behind it. But as you grow, those voices scatter across platforms and departments. Support sees the frustration, sales hears the hesitation, leadership sees the numbers — and somehow, everyone’s looking at the same customers, but no one’s hearing them anymore. That, in my opinion, is the quiet cost of growth. This is the problem Enterpret is solving — by helping teams stay in tune with their customers even as they scale. Here’s how it works: → It collects real-time customer feedback from 55+ channels — support tickets, sales calls, social media (X, Reddit, Instagram, Facebook), app store reviews, community forums, surveys, Slack, and more. → It analyzes all that feedback using AI and tells you exactly what to fix or build next. → It maps everything through a customer knowledge graph that connects feedback, complaints, and requests by channel, user, and payment data. → It even provides a chat interface where you can directly ask questions, and AI agents that flag bugs or issues automatically. That’s why teams like Notion, Perplexity, Canva, Chipotle, and The Farmer’s Dog use it — to make sure customer voices never get lost in the noise. In my view, the real lesson here isn’t about using more tools — it’s about staying close to the people you build for. Here’s how I’d approach it: ✅ Centralize every piece of feedback — even if it’s messy. ✅ Look for patterns instead of isolated complaints. ✅ Use AI systems like Enterpret to uncover the “why” behind what customers say. Because in the end, growth shouldn’t make you deaf. It should make you listen better — just faster. How does your team make sure you’re hearing what customers really mean, not just what they say? #CustomerFeedback #AIProducts #ProductStrategy #VoiceOfCustomer #Enterpret #Leadership

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.

🤖 Revolutionizing AI Evaluation: Agents Judging Agents? Evaluating the performance of advanced AI systems has always been a challenge, especially as these systems grow more complex and autonomous. A recent paper, "Agent-as-a-Judge: Evaluate Agents with Agents" by researchers from AI at Meta and KAUST (King Abdullah University of Science and Technology) introduces a groundbreaking framework where agentic systems evaluate other agentic systems. This approach provides rich intermediate feedback, enabling more nuanced and scalable evaluations compared to traditional methods like human judges or LLM-as-a-Judge. 🔆 Key highlights:- 👩💼 The Agent-as-a-Judge framework mimics human evaluation by considering the entire decision-making and action trajectory of AI agents, not just final outcomes. ✨ The framework introduces DevAI, a benchmark with 55 realistic AI development tasks, complete with 365 hierarchical user requirements for rigorous testing. 👉 Results show that Agent-as-a-Judge aligns closer to human consensus (90%) than LLM-as-a-Judge (70%). 🥇 Additionally, Agent-as-a-Judge offers two key advantages:- 1️⃣ Automated Evaluation: Agent-as-a-Judge can evaluate tasks during or after execution, saving 97.72% of the time and 97.64% of costs compared to human experts. 2️⃣ Provide Reward Signals: It provides continuous, step-by-step feedback that can be used as reward signals for further agentic training and improvement. 🌟 This concept could transform how we assess dynamic, multi-step AI systems, unlocking new possibilities for self-improvement and real-world applications. ♾️ If you're passionate about AI innovation and ethical evaluation, I highly recommend diving into this magnificent work:- 📜 Paper - https://lnkd.in/dDN8se_5 🤗 Dataset - https://lnkd.in/d2eYKEJH 📂 Project - https://lnkd.in/dz3BGr3k What are your thoughts on using AI to evaluate AI? Is this the beginning of a self-regulating AI era? #AI #ArtificialIntelligence #Evaluation #AgenticSystems #AIInnovation #aisafety #aialignment

Most teams drown in feedback and starve for insight. I’ve felt that pain across CX, SaaS, retail—and especially in gaming, where Discord, reviews, and LiveOps telemetry never sleep. The unlock wasn’t “more data.” It was AI turning feedback → insight → action in hours, not weeks. Here’s what changed for me: Ingest everything, once. Tickets, app reviews, Discord threads, calls, streams—normalized and de-duplicated with PII handled by default. Enrich automatically. LLMs tag topics, intent, and aspect-level sentiment (what players love/hate about this feature in this build). Act where work happens. Copilots draft Jira issues with evidence, propose fixes, and close the loop with customers—human-in-the-loop for quality. Measure what matters. Not just CSAT. In gaming: retention, ARPDAU, event participation. In other industries: conversion, refund rate, cost-to-serve. Gaming example: a balance tweak drops; AI cross-references sentiment from Spanish/Portuguese Discord channels with session logs and flags a difficulty spike for new players on Android. Product gets a one-pager with root cause, repro steps, and a recommended hotfix—before social blows up. That’s the difference between a rocky patch and a win. This isn’t just for studios. Healthcare, fintech, DTC, SaaS—same playbook, different telemetry. I put my approach into a 2025 AI Feedback Playbook: architecture, workflows, guardrails, and a 30/60/90 rollout you can start tomorrow. If you lead Product, CX, Support, or LiveOps, it’s built for you. 👉 I’d love your take—what’s the hardest part of your feedback loop right now? Link in comments. 💬 #AI #CustomerExperience #Gaming #LiveOps #ProductManagement #VoiceOfCustomer #LLM #Leadership #CXOps

One of my favorite new use cases for AI is for pressure-testing marketing copy and collateral. Over the past few weeks at Sequel.io, I’ve been building a "synthetic buying committee" GPT inside ChatGPT - a custom model trained on: - Transcripts from customer and prospect calls - Broader audience research into our personas - G2 reviews and customer quotes - Notes from discovery conversations and win/loss analysis All of that became the foundation for a simulated group of buyers that mirrors Sequel’s real personas (CMOs, demand gen leaders, and marketing ops pros). Now, whenever we create a new asset (an ad, landing page, or email) I can feed it into the GPT and ask: “Give me persona-by-persona feedback. What resonates? What misses? What would make this stronger?” I did it this morning for a new landing page tied to an ad campaign and within seconds, it returned feedback specific to three personas, with individualized critiques and a consolidated set of overall recommendations to strengthen conversions (our goal - demo requests). Then, I asked it to rewrite the copy incorporating those changes, and it nailed the structure. Afterward, I asked it for universal best practices to apply to any new landing page the team creates. Here’s what it gave me: 1️⃣ Clear pain + business impact 2️⃣ A single differentiator that creates the “aha” moment 3️⃣ A value story (pipeline, efficiency, experience) 4️⃣ Buyer language, not vendor language 5️⃣ Before / after transformation 6️⃣ Strong social proof throughout 7️⃣ Multiple CTA insertion points 8️⃣ Emotionally resonant messaging for each persona 9️⃣ Visual clarity + narrative simplicity 🔟 A results-oriented CTA Pretty spot on, IMHO. Too often, marketers ship campaigns without getting critical feedback. Sometimes its because they feel like they don't have the time, and sometimes its because they're afraid to share their work. AI is great at solving for both of those challenges. Feedback is fast (almost instantaneous), unemotional, and relatively unbiased. That said - and I can’t emphasize this enough - AI is NOT a replacement for human judgment. You still need someone with taste, context, and intuition to edit, refine, and make the final calls. But it IS a powerful tool for pressure-testing creative and spotting blind spots before you hit publish. I’ll be using this workflow a lot more in Sequel’s marketing and would love to hear from anyone else who has built something similar on how you're using it in your day-to-day. #AI #marketing #kathleenhq

Two brothers from India built a $25M AI company in June 2020.   2.5 years before ChatGPT launched.   Today, they process feedback from 220 million users for Canva, Notion, and Figma. Their AI cuts Voice of Customer analysis from 2 weeks to 3 days.   𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺   Varun Sharma was employee at Amplitude and watched product teams track retention but couldn't figure out why customers churned.   Feedback scattered across 55+ channels: support tickets, sales calls, Slack, social media, app reviews.   Product teams knew what happened. Never why.   So both brothers built Enterpret. Arnav's NLP background from Uber + Varun's go-to-market insights = custom AI for feedback.   𝗛𝗼𝘄 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁 𝗪𝗼𝗿𝗸𝘀   Custom NLP models trained on your specific feedback. Not generic sentiment analysis.   Auto-ingests from 55+ channels every 24-36 hours: → Zendesk, Intercom, Gong, Chorus → Slack, Discord, Reddit, Twitter → App Store reviews, sales transcripts   Creates adaptive taxonomy that updates bi-weekly. Learns from your corrections.   𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝗶𝗲𝘀   Canva: 200+ employees, 20,000+ queries in 6 months. Product Manager: "Get top issues from past 30 days in 20 seconds."   Notion: 2 weeks → 3 days for monthly insights. Before: 700+ tags nobody trusted. After: identify issues critical enough for dedicated engineering teams.   Browser Company: Full day → 30 minutes for research synthesis.   𝗪𝗵𝗼 𝗜𝘁'𝘀 𝗙𝗼𝗿   Built for product-led companies treating customer intelligence like product analytics—critical infrastructure.   Enterprise tool. Enterprise pricing ($120K+). Enterprise complexity (4-8 week onboarding).   𝗧𝗵𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆   They built custom AI models in 2020 when most dismissed NLP for feedback. They focused on one problem: why customers churn.   Today: 2+ billion conversations monthly.   Over to you: Do you actually know why your last 10 customers churned?

Is AI feedback the secret to skyrocketing performance or a potential trust breaker? Just read a fascinating study in the Strategic Management Journal that’s got me thinking about the future of AI in the workplace. Siliang (Jack) Tong and colleagues uncovered some interesting insights: ⇢ AI-generated feedback boosted employee performance by an impressive 12.9% compared to human feedback. ⇢ But, disclosing that feedback came from AI led to a 5.4% performance drop. Ultimately, perception matters! ⇢ AI provided higher quality, more relevant recommendations, identifying more mistakes and offering actionable steps. ⇢ However, employees trusted AI feedback less and worried more about job displacement. So, what does these findings mean for leaders navigating the AI revolution? ↳ Leverage AI’s feedback capabilities – the performance enhancement is significant. ↳ Address employee concerns proactively, particularly considering that trust is crucial for AI adoption. ↳ Consider a tiered approach. Specifically, AI feedback for experienced staff, human feedback for newcomers. ↳ Communicate clearly about AI’s role in enhancing, not replacing, human work. Ultimately, AI in management of employees is a balancing act. We need to harness its potential while nurturing employee trust and engagement. What’s your take? Will AI feedback help or hurt employee morale in the long run? #FutureProofYourLeadership #AImanagement #employeeperformance #leadershipinsights #AIrevolution

Struggling to make sense of customer feedback? Here’s how AI can help. If you’ve ever felt overwhelmed by a pile of testimonials, reviews, or survey responses, you’re not alone. Most small business owners know there are insights in there… but don’t have time to dig them out. That’s where AI tools like ChatGPT and Gemini come in. Here’s how to use them to quickly find patterns, improve your messaging, and understand what really matters to your customers: Collect your feedback Export your Google reviews, email testimonials, or survey responses into one document. It doesn’t have to be perfect — just copy and paste. Ask AI to summarise themes Prompt example: 🗣️ “Can you identify the top 3 strengths and 3 weaknesses mentioned in these customer comments?” You’ll get a quick snapshot of what’s working (and what’s not). Dig deeper into emotions and language Prompt example: 🗣️ “What language or phrases do customers use when describing why they chose us?” Use these phrases in your website copy or ads — it's literally your customers telling you what resonates. Look for objections and concerns Prompt example: 🗣️ “Are there any common objections, frustrations or hesitations mentioned in these reviews?” You can then address these in your FAQs, emails, or onboarding flow. You don’t need to be a tech expert. You just need to ask good questions. If you’re already using AI for content, try pointing it at your feedback. You might be surprised at what you learn. #aimarketing #chatgpt #gemini

I was flabbergasted when I saw the first results of an AI-tool for reviewing an empirical NCA paper. Why? Because the quality of the feedback was close to that of a human reviewer who is familiar with NCA, and substantially better than feedback from an average human reviewer (who is not familiar with NCA). And because the feedback came within minutes after uploading the paper to the AI platform. I am talking about our new AI-SCoRe tool developed by Jon Bokrantz, Gijs Van Biezen and myself that is based on the interactive #SCoRe checklist and trained with the content my new NCA book. It can be used as follows: 1.Upload your paper about an empirical NCA study to an AI-platform like Claude, ChatGPT, Gemini, etc. 2. Apply the tool in one of two possible ways: - Single use Regular AI-SCoRe:  Paste the tool's user prompt into the prompt field of the AI platform. - Multiple-use System AI-SCoRe: Upload the tool's system prompt to the AI platform and use is as Skills in Claude (and possibly Codex in ChatGPT)  3. Get the results within minutes. The quality of the output depends on the power of your AI model. The tool is meant for authors to improve their manuscripts. Read the short note "About NCA" before you start. It tells how AI-SCoRe works, how it was developed, and gives warnings, and limitations that the user should know. Try it out at https://lnkd.in/esNvg675 under Extra's - AI-SCoRe and give us feedback so we can improve the tool.