Work you choose
Big bets start with a hypothesis about what the product should become. Confidence Loop gives you the user evidence up front, so you can start exploring with the relevant issues and observations already in hand.
Features / Confidence Loop
Products that fix themselves, with your team in the loop.
Confidence Loop turns session recordings, analytics, support tickets, and error logs into concrete fixes. It investigates, builds, and proves each one, with your hands on the wheel.
Confidence Loop handles the paper cuts so your team can focus on the bets
Confidence Loop helps with both kinds of product work: the bets you choose and the fixes your users point to.
Work your users point to
Paper cuts, recurring errors, stale docs, small missing features. Confidence Loop notices these, investigates them, and fixes more of them over time, so your team can spend its time on the bets.
Small problems pile up because nobody has time to connect the evidence
Every failed action, error log, support chat, and feature request tells you something about your product. One ticket is easy to dismiss. Twelve tickets, a sales call, and an error spike about the same screen show you exactly where the product falls short.
That evidence is spread across the help desk, Slack, the error tracker, and call recordings. Someone has to see it, connect it, figure out the cause, and decide what to change. Most teams don’t have that attention to spare, so the small fixes wait, and users keep running into them.
How does Confidence Loop work?
Confidence Loop takes each problem from user signal to observation, issue, investigation, and improvement. Every step keeps a link back to the user evidence behind it, so you can always see why the loop did what it did.
01
Signals become observations
An observation is a single, concrete fact about your product taken from a user signal: a bug, a feature request, a usability problem, or a pain point. Confidence Loop reads each signal, extracts the observation, and keeps the original excerpt and source attached. Nobody has to watch every session recording or comb through every dashboard to find what matters.
02
Observations cluster into issues
An issue is a group of observations that point to the same underlying problem. When ten users get stuck on the same step across session recordings, analytics events, and support tickets in your warehouse, Confidence Loop groups them into one issue with all ten pieces of evidence. You prioritize one problem with its full weight of evidence, instead of ten scattered complaints.
03
Agents investigate
For each issue, an agent gathers the evidence, looks at the relevant parts of the product, and works out the likely cause. It proposes a next step: a code change, a design change, a docs fix, or escalation to a product owner. Design changes come with wireframes of the options. The diagnosis is done before anyone picks up the issue.
04
Improvements ship
When the fix is clear and your autonomy settings allow it, Confidence Loop builds the change with Confidence Code, Cursor, or Claude Managed Agents and sends it for review. The evidence chain stays attached, so a reviewer can trace the change back to the users who hit the problem.
Can Confidence Loop use data from my data warehouse?
Yes. Confidence Loop works with BigQuery, Databricks, Redshift, and Snowflake, and can use any data in your warehouse as a signal source: product events, error tables, churn and cancellation reasons, NPS responses, survey results.
Confidence already computes experiment results directly in your warehouse. Confidence Loop works the same way, so there’s no new pipeline to build and no data to copy into another tool.
When does Confidence Loop ask a human?
Confidence Loop asks for a decision when there's more than one reasonable fix, or when a small change could affect something larger. You get the problem, the evidence, the investigation, and the options in one place, and you make the call.
How much does Confidence Loop do on its own?
You choose how far the loop runs. Start at Collect and see which issues the loop surfaces. Raise the level as it proves reliable on your product.
01
Collect
Surfaces issues for your team to work on.
02
Investigate
Diagnoses issues and proposes solutions.
03
Build
Builds fixes and sends them for review.
04
Full
Closes the loop from signal to shipped fix.
How does Confidence Loop connect to experiments?
Confidence Loop runs on the same platform as your A/B tests, feature flags, and metrics. An experiment tells you whether the metrics you chose moved.
After the rollout, Confidence Loop picks up what those metrics don’t track: the sessions where users get stuck, the error events, the support tickets in your warehouse. What it finds becomes evidence for the next hypothesis.
Questions and answers
What signal sources does Confidence Loop support?
Out of the box, Confidence Loop uses the session recordings and product analytics built into Confidence. It can also use any data in your data warehouse as a signal, so support tickets, survey responses, or error events you already sync there work today. Direct connectors for the most common signal sources are coming soon.
Does Confidence Loop change my product without approval?
Only if you allow it. At the Collect, Investigate, and Build levels, a person reviews every change before it ships. Changes ship without approval only when you set the level to Full.
How is Confidence Loop different from a customer feedback tool?
Feedback tools organize what users say so that people can act on it. Confidence Loop also investigates the cause, proposes or builds the fix, and lives on the same platform as your experiments and feature flags.
Which coding agents does Confidence Loop work with?
Confidence Loop builds fixes with Confidence Code, the coding agent that comes with Confidence. It also integrates with Cursor and Claude Managed Agents, so teams can hand fixes to the agent they already use.
Can I use my own data warehouse as a signal source?
Yes. Confidence Loop supports BigQuery, Databricks, Redshift, and Snowflake. Any table in your warehouse can feed it, from product events to churn reasons, with no new pipeline.
How does Confidence Loop know which team owns an issue?
You define your product surfaces and the team that owns each one. Confidence Loop identifies which surface an issue affects and routes it to that team. As people reassign issues, it learns who handles which kinds of problems and routes similar issues directly to them.
Can I trace a fix back to the users who reported it?
Yes. Every improvement links to its investigation, its issue, the observations behind the issue, and the original signals.
Read more about Confidence Loop
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.
Johan Rydberg
The product loop
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.
Johan Rydberg
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