Zeer
TalviewTalview buying intent
4 tracked signals — top 3 topics below — Engineering is carrying most of it.
Attention by team
taxonomy_intent_rollup × social_profile.roleEvery tracked signal from a Talview 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.
12d ago
12d ago
25d ago
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We track the full taxonomy across every account in the graph — including themes not shown on this page.
Who to contact at Talview
verified title on filePeople at Talview 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 Talview
names withheld on the public pageCompanies whose people mention Talview 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 TalviewReal public activity that surfaced Talview in a tracked topic. Not a sentiment score — just what people actually wrote.
"AI will replace QA engineers." I hear this a lot. Here's what I actually experience day-to-day: AI created MORE testing problems to solve, not fewer. I'm a Senior Test Engineer at Talview. We build AI-powered proctoring software. My job isn't just to test the app, it's to test the intelligence inside it. That means asking questions like: 1. Does this face detection model work when the camera angle changes? 2. Is the object detection confident enough, or just lucky on clean data? Traditional test cases don't answer these questions. You need a different mindset, one that treats model behavior as the product. This is where QA is heading. Looking for Senior QA / SDET / AI Testing roles where I can bring this experience. Open to conversations. 👋 #QAEngineering #AITesting #MachineLearningTesting #Playwright #TestAutomation #OpenToWork #Bangalore
SmartRecruiters just announced autonomous talent acquisition, with agentic interviewing, engagement orchestration, and candidate verification built in. The word "autonomous" is doing a lot of work in that sentence. You can build a hiring system that runs at 10x throughput. You can automate sourcing, screening, interviewing, scheduling, and offer management. But you cannot automate away the fact that a bad input at 10x scale becomes a 10x bad hire. Phenom is building fraud detection agentic AI. Hirevue is deepening their verification layer. Every serious HR platform now mentions candidate verification in the same breath as autonomy. They are not redundant concepts. They are opposing forces in equilibrium. The candidates who slip through to offer are the ones the system is confident about. The faster the system moves, the more confidence becomes dangerous. Confidence at speed requires proofing. Not just the claimed skills, but the authenticity of the signals themselves. That is harder than it sounds. Most organizations have not reckoned with this yet. They are focused on speed and volume. The ones who will actually execute autonomous hiring without blowing up their offer-to-start ratio are treating verification not as a gate at the end but as a distributed signal throughout the flow. That is a fundamentally different system design. The question is not whether autonomous hiring works. It works. The question is what you are verifying before you scale it. Visit https://lnkd.in/dikUUgZj
There is no guarantee that the candidate who aced the interview is the one who is showing up for joining.