# Case Study: How Canonical Labs Scales Risk Intelligence > Discover how Canonical Labs, a risk-intelligence infrastructure company, transformed their product development by unifying feedback from Slack, support… --- [Skip to main content](#main-content)Case Study # Case Study: How Canonical Labs Scales Risk Intelligence with Product Clarity Discover how Canonical Labs, a risk-intelligence infrastructure company, transformed their product development by unifying feedback from Slack, support, and customer calls with ClosedLoop AI. cl **ClosedLoop AI Team**Nov 12, 2025 · 2 min read [LinkedIn](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Fclosedloop.sh%2Fblog%2Fcanonical-labs-case-study)Copy link On this page - [About Canonical Labs](#about-canonical-labs) - [The Challenge](#the-challenge) - [The Solution: ClosedLoop AI](#the-solution-closedloop-ai) - [The Results](#the-results) - [Why It Matters](#why-it-matters) [Case Study](https://closedloop.sh/tag/case+study)[Product Intelligence](https://closedloop.sh/tag/product+intelligence)[Customer Feedback](https://closedloop.sh/tag/customer+feedback)[Canonical Labs](https://closedloop.sh/tag/canonical+labs)[Risk Intelligence](https://closedloop.sh/tag/risk+intelligence)[Fraud Prevention](https://closedloop.sh/tag/fraud+prevention) ## About Canonical Labs ![Canonical Labs Logo](/images/blog/canonical-labs-logo.png) [Canonical Labs](https://gocanonical.com)builds advanced risk-intelligence infrastructure for high-volume Stripe merchants. Founded by former Stripe engineers, the company helps payments, operations, and risk teams automate fraud prevention and make confident, data-driven decisions at scale. As Canonical grew rapidly, the volume of feedback from customers, partners, and internal teams increased exponentially, creating an opportunity to transform that feedback into structured product intelligence. ## The Challenge - Rapid growth meant more feedback from merchants, ops, and risk teams. - Insights were scattered across Slack threads, support conversations, and partner calls. - Product teams needed a fast, structured way to see which feedback patterns actually mattered. ![Mykhailo Iakovenko](/images/blog/misha.jpg)"As Canonical Labs scaled rapidly, the volume of feedback from our ecosystem exploded. ClosedLoop AI helped us harness it into clear product direction and faster decisions." Mykhailo Iakovenko, CEO, Canonical Labs ## The Solution: ClosedLoop AI Canonical Labs implemented ClosedLoop AI to automatically aggregate and analyze feedback from Slack, support, and customer calls. ClosedLoop AI detected recurring themes across merchant feedback, identified the most impactful product insights, and surfaced them directly inside Canonical's internal workflow tools, enabling the product team to act faster and with greater confidence. **What ClosedLoop AI delivered:** - Unified feedback from all channels in one structured view - Automatic detection of recurring themes across merchant and partner feedback - Clear, evidence-based insights for product prioritization - Instant sharing of data-driven insights within the team ## The Results ✅ **Faster Product Decisions:**Product insights surfaced automatically within hours ✅ **Shared Visibility Across Company:**Risk, ops, and product teams aligned on priorities ✅ **Higher Confidence in Features:**Focus on validated problems, not assumptions ✅ **Sustained Speed:**Continued rapid shipping with data-backed clarity ## Why It Matters In risk intelligence, speed and accuracy determine impact. ClosedLoop AI gave Canonical Labs a unified, evidence-driven view of customer and partner feedback, helping the team build confidently, align faster, and scale product decisions with clarity. ClosedLoop AI Team We build tools that turn customer conversations into product decisions. ClosedLoop AI analyzes feedback from 44 native integrations to surface the insights that matter. [More about ClosedLoop AI →](https://closedloop.sh/) ### Get insights like this in your inbox Product insights delivered weekly. No spam. Unsubscribe anytime. 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