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.
Source-backed 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-reported evidence
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.
Attributed to the cited source; GrowthDex has not independently verified it. Use this as context, not as a forecast, benchmark, or recommended target.
Evidence scope: claims and metrics are attributed to the linked source; GrowthDex has not independently replicated the reported result. Check the original context and validate fit before applying it. Read the evidence methodology.
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.
How to do it
- Choose one target segment and define the exact next action this tactic should prompt.
- Adapt the tactic to Support and Product, using evidence from Canny Changelog as context—not as a promised result.
- Record a baseline for: Users who resolve the issue and complete the next useful action. Set a guardrail that would make you pause the test.
- Run a narrow pilot and choose a test window that fits the normal decision or use cycle; do not scale before you have observed the full cycle.
- Compare the signal with its baseline. Scale only if the intended action improves without crossing the guardrail; otherwise revise one variable or stop and record what you learned.
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