Systems | Development | Analytics | API | Testing

Meet Winny, a GTM Intelligence agent built with AgentSpot and ThoughtSpot. See how teams can get faster answers to questions about conversion and pipeline velocity by simply asking questions in AgentSpot or Slack, with verified data pulled directly from ThoughtSpot. What is AgentSpot? AgentSpot is multiplayer AI for your business. Anyone can build, share, and collaborate with AI agents connected to your company’s data, context, and tools.

Most companies are investing in AI. Very few are seeing it reflected in their financial results. The gap comes down to three foundational decisions. In this clip from The Data & AI Chief, Raman Tallamraju from Vanguard breaks down what the companies seeing measurable AI benefits have in common.

Most organizations are moving fast on AI ambition. Fewer are moving fast on what makes that ambition possible. Before you can reimagine your business with AI at its heart, your data estate needs six things: to be well-defined, trusted, well-connected, contextualized, consumed in a multimodal way, and ready for both humans and machines at scale. Most organizations have two or three. The ones pulling ahead in AI have all six.

New hires always have questions, and waiting on a response slows people down on day one. In this video, we use AgentSpot to build an Employee Policy Helpdesk Agent that pulls from your internal knowledge base and policy pages to answer everyday HR questions accurately, without paraphrasing or guessing. It knows when to escalate to a human and deploys directly in Slack so employees get answers right where they already work.

The dashboard looked perfect. It was AI-generated, visually stunning, the kind of output that would sail through a stakeholder review without a second glance. Then, during a live stream, someone pointed out the KPI in the top left corner was wrong. That moment captures one of the most important and underappreciated risks in AI-generated analytics right now. A confident answer isn't the same as a correct one, and in enterprise settings, the gap between the two can be very expensive.

The Thought Tank is back. This time, ThoughtSpot CMO and host Micheline Nijmeh is sitting down with Kelley Jarrett to unpack where Sales Ops gets an edge with real-time data. Join us live on August 26th at 12:30 PM ET. If you're in Sales Ops or RevOps, this one's for you. Kelley will pull back the curtain on how she actually runs pipeline, manages AE performance, and keeps forecast accuracy honest, all from ThoughtSpot.

Watch how AgentSpot builds a Product Discovery Agent that reads support tickets, sales calls, Slack threads, CRM notes and product analytics, clusters what customers keep raising into ranked themes, and hands your PMs the pattern, the accounts affected, the evidence behind it and what to do next. What is AgentSpot? AgentSpot is an Agentic Workforce Platform for building workflows that make decisions, take action, and deliver results across every system your business runs on, grounded in your data.

Watch how AgentSpot builds an Opportunity Planning Agent that reads your validation brief in Confluence, queries your GTM and product models in ThoughtSpot for the evidence behind it, weighs the build options against real pipeline and roadmap themes, and publishes a decision-ready brief with a named bet, a priority call, and a now-next-later path, so your next planning cycle starts from evidence instead of opinion.

Watch how AgentSpot builds a Release Notes workflow that checks GitHub every morning for new staging releases, gathers the merged PRs behind them, rewrites the whole lot into benefit-first, jargon-free release notes, and publishes them as a Slack Canvas with a short summary posted to your channel, so your team learns what shipped without anyone hand-writing it. What is AgentSpot? AgentSpot is an Agentic Workforce Platform for building workflows that make decisions, take action, and deliver results across every system your business runs on, grounded in your data.

Semantic layers and ontologies have moved from nice-to-have data modeling tools to the foundational engine required for enterprise AI. In this episode, Raman Tallamraju, Senior Director and Head of Enterprise Data Architecture and Engineering at Vanguard, breaks down how Vanguard is architecting its AI semantic layer to turn scattered institutional knowledge into reliable, agent-ready context. He shares why autonomous agents expose decades of hidden data debt, how to bridge domain-specific definitions like clients versus prospects, and how to balance building a unified semantic layer with a pragmatic, federated data operating model.