Evidence-first AI visibility
In developmentAI visibility becomes an inspectable evidence chain.
An evidence-first AI visibility product in private development, designed to trace how buyer questions become AI answers, brand mentions, citations and evidence-linked follow-up.
01 / The challenge
Brands need to understand not only whether AI systems mention them, but which questions, sources and comparison conditions shape those answers.
AI visibilitySource evidenceBrand researchMatched follow-up
02 / Product decisions
Decisions before decoration.
- 01
Start with real buyer questions instead of an abstract visibility score.
- 02
Preserve answers, mentions, citations, sources, failures and timestamps together.
- 03
Connect each recommended website action to observed evidence and a compatible follow-up.
03 / The system
What had to work together.
- Buyer-question and market definitions
- AI answer and citation observation
- Evidence-linked improvement workflow
- Matched follow-up comparisons
04 / Responsible delivery
Useful AI needs a boundary and a recovery path.
- 01
Conditions fixed before comparison
- 02
Sources, collection limits and uncertainty kept visible
- 03
Private-development maturity and beta access stated publicly
05 / Evidence
What can be verified now.
- Public product site and evidence method at ekvidu.com
- Ask, observe and improve workflow described openly
- Private-development status and beta-access route visible on the site
Maturity: In development. No adoption, customer or performance metric is implied unless it is stated explicitly.
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