
Confidence Loop: Building products that continuously improve
Confidence Loop turns signals from real users into product understanding and improvements, with teams choosing how much of the loop runs autonomously.
Read article
Confidence Loop turns signals from real users into product understanding and improvements, with teams choosing how much of the loop runs autonomously.
Read article
The Confidence Slack bot puts experiment results, flags, recordings, and analytics in the Slack threads where product questions already start.

Confidence Cloud gives you a data warehouse created and operated by Confidence, so you can define entities, send events, and run experiments without setting up warehouse infrastructure of your own.

Product Analytics in Confidence lets you ask questions of your data in plain language and get answers you can explore with more depth and flexibility than a dashboard.

Confidence Session Recordings lets you replay actual user sessions, with AI-powered analysis to surface bugs and insights right from your experiments.

Many apparent Bayes-versus-frequentist disagreements are really disagreements about goals, estimands, modelling assumptions, or desired decision properties.

Confidence Agent is an AI collaborator built into Confidence that works with your flags, experiments, metrics, and project documents.

As AI increases individual leverage across the product development stack, the boundaries between PM, design, analysis, and engineering are blurring.

Discovery and delivery are no longer sequential phases. AI collapsed the build phase, and now the teams winning are the ones with the tightest loops.

At Spotify, 42% of experiments are rolled back after guardrail metrics detect regressions. The discipline to discard is more valuable than the ability to ship.









