Zeer
H2O.aiH2O.ai buying intent
2 tracked signals — top 2 topics below — Engineering is carrying most of it.
Attention by team
taxonomy_intent_rollup × social_profile.roleEvery tracked signal from a H2O.ai 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.
23d ago
16d 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 H2O.ai
verified title on filePeople at H2O.ai 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 H2O.ai
names withheld on the public pageCompanies whose people mention H2O.ai 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 H2O.aiReal public activity that surfaced H2O.ai in a tracked topic. Not a sentiment score — just what people actually wrote.
SR 26-2 did something quite interesting, it explicitly excluded agentic AI from the scope of model risk management. MRM governs models. Agentic AI isn't only a model. It's a system that uses models to perceive, reason, plan, call tools, act and evaluate — often across many steps, often without a human in sight. Everyone says they want governed agentic AI but governing the model is no longer enough. Typical industry's answer to agentic governance is a stack of precautions that sound reassuring and aren't: - A prompt can suggest policy. It cannot enforce policy. - A memory file can preserve text. It cannot create authoritative state. - An LLM judge can offer an opinion. It cannot verify by rule, number or state transition. - A human can intervene. That does not make the system governed. Stacked together, these mechanisms create a convincing illusion of control. The entire control chain still rests on probabilistic interpretation of text. That means it is vulnerable to instruction drift, prompt injection, retrieval failure, version confusion and correlated model error — all while feeling disciplined. That is not governance. It is the appearance of governance. In high-risk environments, that is exposure. Real agentic governance requires separating thinking from deciding: external executable policy, authoritative state with provenance, typed verification (not one vague judge), tool-layer gating, workflow contracts that cannot be skipped and humans setting the rules rather than approving every keystroke. Until we build that, much of what is marketed as agentic AI governance will remain a persuasive illusion of control rather than control itself. The regulators who oversee the largest banks in the country, in the guidance that replaced SR 11-7 after fifteen years, drew a line around agentic AI and put it outside the MRM frame. Not because it doesn't matter. Because the paradigm doesn't fit and extending it would have let banks check boxes on the standard harness and call the system governed. Full essay on Substack 👇 https://lnkd.in/eaCU5Bq3 #AgenticAI #AIGovernance #SR262 #ModelRisk #MRM #ResponsibleAI