Zeer
GalileoGalileo buying intent
8 tracked signals — top 4 topics below — Marketing and Sales are carrying most of it.
Attention by team
taxonomy_intent_rollup × social_profile.roleEvery tracked signal from a Galileo employee, placed by the team they sit in and the theme they engaged with. Darker means more concentrated attention.
Topics being researched
30-day windowAll tracked topics, ranked by signal volume. Confidence is the classifier's certainty that the signal belongs to this topic.
3d ago
13d ago
9d ago
11d ago
Need intent for a specific topic or industry?
We track the full taxonomy across every account in the graph — including themes not shown on this page.
Who to contact at Galileo
verified title on filePeople at Galileo whose own activity produced these signals. Names are withheld pending a consent decision; titles, seniority and topic are real and free to browse.
Top accounts researching Galileo
names withheld on the public pageCompanies whose people mention Galileo in their own activity. Account names are withheld here; not yet classified as implementation partner vs. genuine prospective buyer.
No buyer signal yet for this account
Nobody in the graph is currently discussing this company by name in a way we can attribute to a specific employer.
Buyer profile
company size · seniorityHow big those accounts are, and who inside them is senior enough to matter. Competitor products still not yet computed for this account.
What's been said
public posts mentioning GalileoReal public activity that surfaced Galileo in a tracked topic. Not a sentiment score — just what people actually wrote.
11 AI startups pitching at Llama Lounge '25 at Stanford University Graduate School of Business . I went in expecting a vendor showcase. I left with one observation I cannot shake. The lineup ran across a wide surface area: agent workforces for food distribution (Anchr), patent infrastructure ( Fearn ), AI for clinical trials (Intake), reasoning infra for physical world AI (Monocle), voice-first developer platforms (Vocal Bridge), search for agentic capabilities ( ZeroClick ), context engines for AI apps ( Redis ), and several others. What separated the pitches that landed from the ones that did not had almost nothing to do with the model. It had to do with whether the founder could name the specific job their customer wakes up worried about, and frame the product as the answer to that worry. The teams who did this well were not selling "AI for X." They were selling "you will not miss your deadline." "Your trial will not fail recruitment." "Your agent will not lose state." Different verticals, same shape. The teams still in the "AI for X" frame have work to do, and most of them know it. Jeremiah Owyang Keith Newman Serena Yu Bobby Napiltonia #AgenticAI #AIInfrastructure