Case study · AI answer-engine visibility (AEO)

Orbit Labs

Audits and fixes how AI answer engines describe and recommend your brand.

SQL volume vs LinkedIn ads
6.2×
Paid audits in first 30 days
5
Demo-show rate
41%
Payback on a $1.2k audit
16×
The storyorbit-labs.net
The challenge

Answer-engine optimization is a brand-new category — buyers don't search 'AEO audit.' Standard ICP filters returned old-school SEO heads who weren't ready to spend on a new line item.

The approach

Saava watches the LinkedIn voices defining the category — AEO researchers, generative-search analysts, growth leaders writing about Perplexity / ChatGPT visibility. Every CMO and head-of-growth engaging with that content arrives scored and contact-enriched.

A new category that the ICP filters don't know about

Apollo doesn't have a 'cares about AEO' checkbox. Filtering by SEO title surfaced people who weren't ready to add another budget line.

Engagement is the only intent signal that exists yet

Until the category matures, the cleanest signal is who's reading the few people writing about it. Saava routes us to those readers the moment they like a post.

Closing the loop before competitors notice

Five paid audits in the first 30 days, at a 16× payback on the audit price. The unfair advantage is that we knew about each buyer before they'd typed the category into a search bar.

Buying signals for AEO live entirely on LinkedIn engagement. Saava reads them in real time — every other tool was guessing at titles that hadn't caught up yet.
Founder, Orbit Labs

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