Zeer
SimScaleSimScale buying intent
5 tracked signals — top 2 topics below — Marketing and Engineering are carrying most of it.
Attention by team
taxonomy_intent_rollup × social_profile.roleEvery tracked signal from a SimScale 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.
13d ago
25d 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 SimScale
verified title on filePeople at SimScale 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 SimScale
names withheld on the public pageCompanies whose people mention SimScale 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 SimScaleReal public activity that surfaced SimScale in a tracked topic. Not a sentiment score — just what people actually wrote.
"How accurate is it?" and “What about hallucinations?” are questions we often hear in Engineering AI and Physics AI customer projects. There are of course technical approaches to ground these models ane ensure accuracy - e.g. confidence scores in our Physics AI UI are an example. But there is a tendency to think that using AI in an engineering workflow bypasses the procedures in use today. More often than not, the downstream process doesn't change - at least not initially: Traditional way: You have time to simulate 5 or 10 design iterations using high-fidelity CFD/FEA. Leveraging an AI workflow: You explore orders of magnitude more iterations in the same or less time to find a design you would have otherwise missed. Once you’ve dialed in the best candidate, you revert to a standard workflow. Running the high-fidelity simulation or doing the physical test - the same certification path always used. AI systems don’t need to replace the validation; they can increase the chances that the design you eventually certify is close to the best possible one. Broadening the search can be done while keeping the rigor. More on this via the link below