AI answers as a member journey
Your members already ask AI assistants how to open an account, whether their deposits are insured, and how your rates compare. Each answer is a journey you do not currently see: sometimes right, sometimes stale, sometimes ending at a competitor. Scanley reads those journeys question by question so you can fix the dead ends.
What each engine actually said, per question, per run, including what it cited and who it pointed to.
A stale rate, a garbled eligibility rule, or a misstatement is flagged as its own finding, distinct from being missing.
Corrections ship as drafts against your public pages and records; nothing touches your systems, and your team approves everything.
Members get the third party's version of your process. The fix is making your own page the quotable one.
Do you need access to our systems?
No. We read only what the public web and the engines already see. No core access, no credentials, no member data. That is the whole architecture, and it is why due diligence goes quickly.
Which surfaces do you measure?
ChatGPT, Perplexity, Claude, and Google's AI Overviews today, each named per measurement with run counts and dates. Anything not measured is marked not run and excluded from every denominator, never counted as a miss.