For your team
See more clearly. Make better decisions.
Decision-ready data is more than a clean table. It has a defined business meaning, a clear scope, visible freshness and enough source context to verify the answer.
- 01
Know which accounts, markets and periods support an answer.
- 02
Keep differences between orders, ads, settlements and vendor metrics explicit.
- 03
Move from a signal to an owner, next action and supporting evidence.
From signal to action
What the numbers mean for your team
01Coverage before confidence
Every important result starts with the Amazon sources and periods that are actually available. ClairInsights makes freshness and gaps visible before a result is presented as complete.
- Account and marketplace scope
- Period and refresh visibility
- Source-specific availability
02Business meaning stays attached
Order time is not settlement time. Ad-attributed sales are not total retail sales. Seller and Vendor Central express different commercial relationships. ClairInsights keeps those distinctions clear while providing the shared context needed for comparison.
- Explicit time meaning
- Consistent currencies and identifiers
- Definitions made for business use
03Every answer points to a next step
See what needs attention, why it matters, who can act, and which numbers support the recommendation. Your team can check the answer before acting on it.
- Prioritized business signal
- Named owner and next action
- Traceable supporting evidence
Next steps
Know where to focus next.
- 01
Can this number be trusted for the selected period?
- 02
What changed enough to deserve attention?
- 03
Who should act next, and what should they verify?
Good to know
What to expect
ClairInsights does not turn unavailable Amazon history into recoverable data and does not erase meaningful differences between sources. Access and lookback depend on Amazon, the account, roles, marketplace and report type.
DeliverySelf-hosted or Managed Delivery
FAQ
Common questions
Does ClairInsights publish its aggregation logic?
No. We explain definitions, scope, evidence and limitations without exposing proprietary implementation details.
Can a user trace an answer back to Amazon data?
Yes. Important outputs retain source identity, coverage and freshness context.
Why is this useful for AI?
AI performs better when metrics have stable meaning, allowed scope, current coverage and evidence that can be checked.