steadybit · ManuelGerding · May 20, 2026
diff --git a/.gitbook/assets/example-report.png b/.gitbook/assets/example-report.png
diff --git a/use-steadybit/reporting/README.md b/use-steadybit/reporting/README.md
@@ -4,55 +4,113 @@ title: Reporting
 
 # Reporting
 
-Steadybit's integrated reporting feature provides you with a comprehensive overview of your experiments, experiment runs and your adoption of Steadybit in terms of users, teams and environments.
+Steadybit's integrated reporting feature gives you a comprehensive overview of your service reliability risk, your experiment activity, and your adoption of Steadybit across users, teams and environments.
+Use it to track progress over time, share evidence with stakeholders, and spot regressions across your infrastructure.
 
-![Example of a report showing experiment run results](../../.gitbook/assets/example-report.png)
+![Example of a report showing average service risk](service-risk-average.png)
 
-You can click on the labels below any report to filter the data shown in the graphs.
+You can filter the reports based on different dimensions (e.g. service, team, environment, service) or use the legend's labels below any report to filter the data shown in the graphs.
 
-All charts can be downloaded as png or pdf files.
+All charts can be downloaded as CSV, PNG or PDF.
+
+## Service Risk
+
+The **Service Risk** reports answer questions like how risky are services on average, how are they distributed across risk levels, and which reliability categories are driving the risk.
+[Learn more about Steadybit's service risk](../services/README.md#risk).
+
+Each report writes a fresh data point per service whenever the service's risk changes, or — at the latest — once per day.
+This means your historical timeline always reflects the property values that were in effect at the time.
+
+You can filter Service Risk reports by 
+* Timeframe
+* Teams
+* Environments
+* Services
+* Service Properties (enum-typed [custom properties](../experiments/properties/README.md)).
+
+This allows to focus on services being tagged, e.g., with `Tier 0 - Mission Critical` and drill down on [reliability categories](../../install-and-configure/manage-service-profiles/README.md#categories) like 'Scalability'.
+
+### Average Risk Over Time
+
+Track the rolling average risk across your services.
+Useful as a single-pane health number to share with stakeholders or to spot regressions when new services are onboarded or a service profile changes.
+
+![Average service risk over time](service-risk-average.png)
+
+### Risk Distribution
+
+See how many of your services fall into the low, medium and high risk levels.
+Easy to share with stakeholders the value of your reliability work by showing trending of services into low risk levels.
+
+
+![Example distribution of services by risk level](service-risk-distribution.png)
+
+### Risk by Category
+
+Break down the average risk per reliability category (e.g. Redundancy, Scalability, Dependencies) merged globally across all service profiles.
+Use this to identify which dimensions of reliability need the most investment across your services — independent of which service profile a service uses.
+
+
+![Example risk by category](service-risk-category.png)
 
 ## Experiment Runs
 
-The Experiment Runs report gives you an overview of all experiment runs that have been executed.
+The Experiment Runs report gives you an overview of all experiment runs that have been executed — including their outcomes, what triggers them, and how they move between completed and failed over time.
+Use it to make experiment activity visible across teams and to spot when chaos coverage starts to drift.
 
 You can filter the reports by the following criteria:
 
 * Timeframe
 * Teams
 * Environments
+* Services
 
 ### Number of Runs
 
 Find out how many experiments your teams have run in total.
 
+![Example number of experiment runs](experiment-runs-number.png)
+
 ### Attack Types
 
 Identify which attacks your teams have used most frequently.
 
+![Example experiment run attack types](experiment-runs-attack-types.png)
+
 ### Trigger
 
 Check out what typically triggers an experiment run, e.g., API, CLI, UI, or schedule.
 
+![Example experiment run trigger](experiment-runs-trigger.png)
+
 ### Result
 
 Drill down into the experiment runs by the result and compare the numbers of completed, canceled, failed, and errored experiment runs.
 
+![Example experiment run result](experiment-runs-results.png)
+
 ### Result (Completed vs. Failed)
 
 Compare the portion of completed experiment runs to failed experiment runs to identify the frequency of identifying issues.
 
+![Example experiment run completed vs. failed](experiment-runs-completed-failed.png)
+
 ### Issues Discovered
 
 Identify how many experiment runs turned from completed to failed. We count experiment failures that were immediately preceded by a completed experiment run.
 
+![Example experiment run discovering issues](experiment-runs-issues-discovered.png)
+
 ### Issues Fixed
 
 Identify how many experiment runs turned from failed to completed. We count experiment runs completed that were immediately preceded by a failed experiment run.
 
+![Example experiment run showing fixed issues](experiment-runs-issues-fixed.png)
+
 ## Experiments
 
-The Experiments report gives you an overview of experiments that have been created in your environment.
+The Experiments report gives you an overview of experiments that have been designed in your environment — how many designs exist, what channels teams use to create them, and which methods (from scratch, template, or advice) they prefer.
+Useful for tracking the spread of experiment authoring across the organization.
 
 You can filter the report by the following criteria:
 
@@ -64,28 +122,40 @@ You can filter the report by the following criteria:
 
 Find out how many experiments your teams have designed in total.
 
+![Example number of experiments](experiments-number.png)
+
 ### Creation Channel
 
 Identify which channel is used the most across your teams to create an experiment: UI, API, or CLI
 
+![Example of experiment creation channels](experiments-creation-channel.png)
+
 ### Creation Method
 
 Identify which method is used the most across your teams to create an experiment: From scratch, template, or advice
 
+![Example of experiment creation method](experiments-creation-method.png)
+
 ## Others
 
-These reports give you an overview of the adoption of Steadybit.
+These reports give you an overview of the adoption of Steadybit across your organization.
 
 You can filter the reports by timeframe.
 
 ### Users
 
 Identify the progress you have made to roll out Steadybit in your organization by seeing the number of invited users.
 
+![Example of users](others-users.png)
+
 ### Teams
 
 Easily report on the numbers of teams having access to a safe Chaos Engineering in your organization.
 
+![Example of teams](others-teams.png)
+
 ### Environments
 
 Find out how many environments you have created to roll out a safe Chaos Engineering across your organization.
+
+![Example of environments](others-environments.png)
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