No-feedback-found audit for AI intake gaps
Keep a visible queue of conversations where the AI found nothing so the team can inspect misses instead of blindly trusting automated intake.
Why this can grow a startup
Automation usually breaks in the quiet misses, not the obvious wins. Canny's no-feedback-found view is useful because it exposes the calls and conversations that were processed without yielding captured feedback, along with the reason. That gives operators a way to see whether the model skipped bugs, misunderstood edge cases, or simply saw noise where a human would have noticed demand. Teams that audit misses keep the queue cleaner and build more trust internally, because product can see not only what the AI captured but also what it ignored.
Company example
Canny added a no-feedback-found view in Autopilot that lists processed calls and conversations where no feedback was captured and links back to the original source.
Source and metric
Source: Canny Changelog · Browse Canny Changelog tactics
The view shows why no feedback was captured and includes a View original link back to the source conversation.
Source discovered: May 29, 2026
When to use it
Use this when Support, Product, Research is relevant to ai ops, feedback capture, quality control and you can run a bounded test with a low budget.
When not to use it
Do not use it as a substitute for customer evidence, a clear owner, or a measurable stop condition. Local platform rules and market behavior still need checking.
Founder checklist
- Read Canny Changelog and identify what is directly supported.
- Choose one channel context: Support, Product, Research.
- Define the test around The view shows why no feedback was captured and includes a View original link back to the source conversation..
- Set an owner, evidence window, and stop condition before launch.
Explore the context
Apply this with an operator
Choose product surfaces that compound distribution without hiding weak activation or retention.