Findings that show their work: what changed, the evidence, what to do.
The analyst layer compares your data with baselines, detects what moved, explains the comparison it made, and ranks the next actions with the reasons listed.
Scope, stated plainly. This is not a chat assistant. Findings come from defined rules and thresholds, not a language model, so the same data gives the same result and every score lists its inputs.
Confident answers without evidence are worse than none.
A tool that says “conversion dropped because of mobile” without showing the comparison invites a debate about whether to believe it.
Analysis you cannot inspect cannot be corrected.
Black-box answers
You cannot tell what was compared with what.
No baseline
A change means little without the like-for-like period.
No sample rule
Small numbers produce dramatic but meaningless swings.
From a comparison to a recommendation.
Compare against a baseline
The last 30 minutes are compared with the same slot on each of the last 7 days, and skipped when the sample is too small.
Detect
Traffic spikes and drops, conversion changes, campaign surges and tracking silence are flagged.
Explain
Each finding states the comparison behind it.
Rank
Priority is additive from named inputs, with reasons listed.
Measure
A baseline is captured so the outcome can be judged afterwards.
Signals it reads
- Traffic and conversion change vs same-slot baseline
- Campaign surges and tracking silence
- Severity and confidence
- Affected pages and scope
- Before/after with minimum sample
- Conservative outcome labels
Intelligence you can act on, with the evidence attached.
Intelligence produced
- Findings with their comparison stated
- Ranked recommendations with reasons
- Alerts and notifications
- Impact measurements that avoid unsupported causality
Recommended actions
- Act on the top-ranked recommendation
- Open the evidence behind a finding
- Assign it, or run an experiment
- Check the measured result later
What changes for the team.
Trust through transparency
You can check every finding.
Repeatability
The same inputs give the same output.
Less noise
Insufficient-sample cases are skipped, not guessed.
A closed loop
Recommendations connect to actions and measured impact.
Who reaches for it, and why.
Analysts
Verify a finding in seconds.
Marketing leads
Review a ranked list instead of a dashboard wall.
Founders
Get a defensible read without a data team.
Related capabilities, tools and reading.
Questions people ask first.
Is this an AI chatbot?
No. It is a deterministic analysis layer. There is no conversational interface today.
Does it use a language model?
No language model generates findings. Rules and thresholds do, and the inputs are shown.
Why not use a model?
Repeatable, inspectable results are more useful for decisions than fluent but unverifiable prose.
See what is moving your numbers.
Install one tracking snippet and start reading your own data. Or ask for a walkthrough first.