Systems | Development | Analytics | API | Testing

[AgentSpot Showcase Series] Winny - GTM Intelligence Agent

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.

Is Your Data Estate Actually Ready for AI? The 6 Characteristics That Matter

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.

AgentSpot HR Use Case - Resolve HR Helpdesk Tickets

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 AI Dashboard Looked Perfect, Then Someone Spotted the Wrong KPI

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.

Thought Tank: How Sales Ops Teams Win with Real-Time Data

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.

AgentSpot for Product - Product Discovery

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.

AgentSpot for Product - Opportunity Planning Agent

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.

AgentSpot for Product - Release Notes Workflow

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.

The Data Differentiator: Vanguard's Playbook for AI-Ready 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.

Who's Responsible When AI Gives You the Wrong Answer? Andy Cotgreave and Francois Lopitaux Debate

If an AI agent gives a business user the wrong number, and a consequential decision gets made off it, who's responsible? The data analyst who built the semantic layer? The platform? Or the business user who asked the question and acted on the answer?

AgentSpot Retail Use Case - Dealership Inventory and Pricing App

Traditional dashboards and static reports are not built around the person reading them. In this video, we introduce Data Apps in AgentSpot, a new surface alongside Agents and Workflows, and walk through a Used Car Lot Operations App built for a regional dealership manager. It pulls from ThoughtSpot data models and Slack conversations into a single narrative, scopes every app to the viewer's own credentials so people only see the data they have access to, and supports real interactivity like KPI card drill downs, custom filters, and what if pricing analysis.

Agent Product Use Case - Slack & Jira Discrepancy Workflow

Customers and champions report problems in Slack, but if nobody files the ticket, the issue disappears before it ever reaches Jira. In this video, we use AgentSpot to build a Slack to Jira Coverage Workflow that reads your champions channel every morning, cross-compares it against your Jira backlog, and emails you a report of every issue raised in Slack that no one has filed yet. 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.

SQL-Shaped Intent: The Engineering Behind AgentQL

Our CEO recently wrote reaffirming an architectural decision ThoughtSpot made when LLMs first emerged: we do not use LLMs to directly generate SQL. My team has spent the better part of a year building AgentQL: a capability that doubles down on our decision. So let me explain what we actually built, why it doesn't just honor that architectural decision but depends on it, and the engineering choices underneath.

AgentSpot for Finance - Automating Lease Accounting Agent

Discover what’s possible with AgentSpot as Sheila showcases an AI agent ("Leasey") built to automate lease accounting. From analyzing contracts to creating calculations, schedules, and audit documentation, this workflow shows how teams can use agents to streamline everyday business processes. 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.

Building in the Fast Lane: How AI and Internal Innovation Birthed AgentSpot

The journey to AgentSpot didn't start with a traditional product roadmap or a speculative “what if” from our R&D labs. Instead, it was born out of a growing friction within our own walls and became a "frontier R&D project" fueled by engineers exploring the internal potential of generative AI. When we first launched SpotGPT, our internal genAI application (similar to ChatGPT, but trained on internal resources) we saw immediate and massive adoption.

Introducing AgentSpot: Your Workforce, Multiplied

Your team already knows when a campaign starts to underperform, when spend spikes, or when web traffic shifts. What you don't have is the speed to turn that insight into action. Someone still has to investigate, decide what to do, pull in the right people, and coordinate the work. That takes time. As a data-driven CMO, I've lived this every day. I can know the instant something changes in the data, but there's still a large gap between insight and action. Today, we're closing that gap.