How We Work, And What We Can Prove
We publish only outcomes we can substantiate. This hub holds our public audit sample, our source-ledger methodology, and our own citation-to-revenue operating ledger.
Public Audit Sample
An ungated, truthful sample of the One-Page AI Citation Audit, run on DataDab's own page, so you can judge the deliverable before you buy.
See the One-Page AI Citation Audit →Source-Ledger Methodology
Every claim we publish is tied to a dated source, a calculation, and a scope caveat. We don't publish outcomes we can't verify.
Read our pricing and engagement options →Citation-to-Revenue Ledger
We treat DataDab as the operating experiment: our citation footprint, AI referrals, and captured revenue, published with scope caveats rather than a winning narrative.
Read our AI citation research →Previously Reported Outcomes
Earlier case-study pages reported outcomes we could not substantiate. Those figures are under review and are not published as verified results as we consolidate this surface.
Order a verified One-Page AI Citation Audit →Decision-stage feedback from people who hired us
"We had traffic. We had leads. We did not have a way to explain why we were the right choice when buyers sat down with us and our top three competitors. DataDab rebuilt the comparison story from scratch. Sales cycle dropped by a third within one quarter."
"The first time a prospect told me 'I read your ChatGPT citation analysis and forwarded it to my CEO' was the moment I knew this was a different kind of engagement. It changed how the whole company talks about what we do."
"Amit does not start with tactics. He starts with where buyers actually make decisions, then builds content to be present at those moments. The work paid back in the first six weeks."
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