SaaS Early Adopters Podcast Episode 4 скачать в хорошем качестве

SaaS Early Adopters Podcast Episode 4

SaaS Early Adopters in Conversation with Quebot I'm Akansha, co-founder of Quebot. My co-founder Suraj and I previously worked together at Quizizz. The Product We're building an AI-powered feedback intelligence platform that helps product teams: Consolidate user feedback from every source (support tickets, Slack, interviews, surveys, calls) Automatically surface patterns and themes from thousands of data points Track sentiment and frequency — know what's hurting users most Make product decisions backed by evidence, not gut feel We're currently piloting with a few teams and would love to exchange insights with fellow builders and PMs here. Our Conversation Questions 1. What inspired you to build this product? At Quizizz, we were building for millions of US teachers. The hardest part wasn't engineering — it was understanding what users actually needed. Feedback existed in Intercom, Slack, NPS surveys, user interviews, and sales calls. No single source of truth. PMs were making decisions based on whatever feedback they happened to remember. 2. Who is this product for, and what problem does it solve for them? Product managers and product teams at companies that talk to their users a lot — especially consumer product companies. The problem: feedback is scattered across 5-10 tools, nobody has time to read through all of it, and prioritization becomes a guessing game. 3. What makes your product different from what's already out there? Most feedback tools (Canny, UserVoice) ask users to submit structured requests through a portal. That works in theory but breaks in practice. Real feedback comes in messy, unstructured, through channels people already use. Quebot doesn't change where feedback lives. It pulls from where it already exists and uses AI to do the heavy lifting, extracting themes, detecting patterns, tracking sentiment automatically. 4. Was there a defining moment when you knew you had something worth launching? We built an early version as a Slack bot inside Quizizz. Within weeks, 50 PMs in the org were using it daily. Nobody asked them to. They just found it useful. That's when we knew. 5. What challenges did you face building this product, and how did you overcome them? The biggest challenge is extraction quality — making sure the AI picks up what actually matters and doesn't miss important signals buried in long conversations. We're building feedback loops and review mechanisms to continuously improve this. 6. How did you decide what features to launch with? We focused on the core loop: ingest feedback → extract patterns → show what matters. No roadmapping, no project management, no feature voting. Just the intelligence layer that's missing from every PM's workflow today. 7. Can you give us a quick walkthrough or demo of how it works? Connect your feedback sources → Quebot automatically reads through everything → You see a dashboard of themes ranked by frequency and sentiment, with real user quotes backing each one. Instead of "I think users want X", you get "47 users mentioned X this month, here's what they said." 8. What's one customer story or piece of feedback that really stuck with you? A PM at Quizizz told us: "I used to spend half my week reading through support tickets to understand what teachers are struggling with. Now I just open this and it's all there." That's exactly the feeling we want every PM to have. 9. What's your vision for the next 6 to 12 months? Get the core product rock solid with 10-15 pilot partners. Build integrations with the tools teams already use — Slack, Intercom, Zendesk, Gong. And solve the "closing the loop" problem, when a feature ships, automatically notify the users who asked for it. 10. Where can people try it or learn more? DM me right here. The Offer Your team can try Quebot at no cost, connect your feedback sources, see patterns surfaced automatically, and experience what evidence-backed product decisions feel like. This isn't a limited demo - it's the full product, offered free to early adopters so we can learn from your feedback and build alongside you. If you're a PM or product leader dealing with feedback chaos, just DM me and I'll get you set up personally.