Solutions / Data scientists

Methodology you can trust. Defaults you don't have to fight.

CUPED with the Negi-Wooldridge 2021 estimator. Always-valid sequential testing. Guardrails and SRM checks on by default. Built by a team that has run 10,000+ experiments a year for 15 years.

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Rigorous defaults, not opt-in features

Advanced CUPED

Negi-Wooldridge 2021 full regression estimator. Tighter confidence intervals than the original CUPED formulation. Variance reduction is automatic — no manual covariate selection.

Always-valid sequential testing

Make decisions at any point during an experiment without inflating false positive rates. No fixed-horizon requirements. Stop early when results are clear.

Guardrail metrics

Define the metrics that must not regress. Guardrails run automatically on every experiment. At Spotify, 42% of experiments are rolled back after guardrail checks.

SRM checks

Sample Ratio Mismatch detection on by default. Catch traffic allocation bugs before they corrupt your results. No manual validation required.

Warehouse-native analysis

Experiment results computed directly in your warehouse. Query raw data with SQL. Build custom analyses on top of the same assignment and exposure tables Confidence uses.

AI-powered experiment reports

Automated narrative summaries of experiment results. Highlights significant findings, guardrail violations, and metric movements — generated from the statistical output, not templates.

The experiments you forget to configure are the ones that cause problems

Most experimentation platforms offer guardrails, SRM checks, and sequential testing as features you opt into. Confidence ships them as defaults you'd have to opt out of.

At scale, the difference is enormous. When your organisation runs thousands of experiments, the ones that ship bad results aren't the ones your senior data scientists designed carefully — they're the ones a product manager set up on Friday afternoon without thinking about guardrails.

Confidence's opinion: the platform should protect you by default. Rigour should not depend on who sets up the experiment.

From hypothesis to decision

Design

Define your primary metric, guardrail metrics, and minimum detectable effect. Confidence calculates required sample size and expected runtime. Sequential testing means you can check results at any point.

Monitor

Watch experiments in real time. Always-valid confidence intervals update continuously. SRM checks run automatically. Guardrails alert you before regressions ship.

Analyse

Results are computed in your warehouse. Export to Jupyter, R, or your BI tool. Build custom analyses on the same data. The platform gives you the statistical summary; you go deeper when you need to.

Decide

Ship, iterate, or kill. The platform surfaces the evidence. AI-generated reports summarise findings for stakeholders who do not read p-values. The decision stays with the team.

Built for rigorous experimentation

42%: Caught by guardrails
10K+: Experiments/yr at Spotify
15 yrs: Methodology refined
Always-valid: Sequential testing

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Experiment like Spotify.

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The experiments you forget to configure are the ones that cause problems

Most experimentation platforms offer guardrails, SRM checks, and sequential testing as features you opt into. Confidence ships them as defaults you'd have to opt out of.

Confidence's opinion: the platform should protect you by default. Rigour should not depend on who sets up the experiment.