ThoughtSpot

Your AI analyst just got a major upgrade More business teams are doing their thinking inside Claude and ChatGPT than ever before. Research, planning, analysis, content: it's all happening inside LLM platforms now. But the moment someone needs an answer grounded in actual enterprise data, the workflow breaks. They leave the AI, open the BI tool, run the query, copy the result back. Context lost, momentum killed. That's the problem we set out to solve when we launched ThoughtSpot's Agentic MCP Server back in July. Since then, Spotter , the most trusted agent for enterprise analytics,has gone through multiple major upgrades, and this release brings the full Spotter 3 experience to Claude, ChatGPT, and any MCP-compatible platform or custom agent you build. What started as the ability to ask data questions and build Liveboards now includes deep research, root-cause analysis, code execution, and AI-generated summaries. Same governed analytics, grounded in your semantic layer a

Foreign Filing filed 2026-04-28

Foreign Filing filed 2026-04-28

And what you can do about it Most teams deploying AI agents on their data are watching the wrong things. They check whether the query ran and whether the number looks plausible. When both checks pass, the agent gets credit for a correct answer, and the output flows into dashboards, decisions, and the next agent in the chain. There's a gap between those two checks and actual correctness, and it's where the expensive mistakes live. Getting to a correct answer requires more than a formally valid calculation. It requires the business context around the number: what the column actually represents, which definitions and rules apply, and whether the data being queried is the data the business considers authoritative for the question being asked. The invisible mistake Consider a question a Monday-morning VP might produce: How many active users did we have last quarter in the Northeast? An AI agent inspects the warehouse, locates a column called active_users_v2, writes a clean query,

4,096 Tasks completed 89.8% success rate 302 Active users 4× growth Jan→Mar 86 Agents deployed 73 built by engineers 72 days In production 15,896 messages Modern engineering teams face a familiar paradox: the bigger the system, the more time engineers spend managing the work rather than doing it. Bugs pile up faster than they can be triaged. PRs wait days for review. On-call engineers spend hours reproducing what someone already debugged six months ago. The knowledge to solve all of this exists, but it’s scattered across Jira comments, Confluence pages, Slack threads, and Grafana dashboards. You just never have it when you need it. At ThoughtSpot, we build systems that help you make faster decisions from your data. We decided to apply that same principle internally: build a platform that puts the right engineering intelligence in front of the right person at the right moment. The result is SpotDevOps , an AI SDLC platform that embeds specialized agents across the entire soft

In 1799, soldiers near Rosetta, Egypt, unearthed a stone carved with the same decree in three scripts: hieroglyphs, Demotic, and Ancient Greek. Because scholars already understood Greek, it unlocked a language—and with that, a civilization’s worth of knowledge that had been dark for over a millennium. We’re at a similar inflection point in enterprise data. Organizations sit on vast scores of unstructured data—tables, views, metrics, joins—but the systems reasoning about it don’t inherently understand what any of it means. A column named "rev_adj" could mean adjusted revenue, or something entirely different, depending on the team, the region, or the fiscal calendar. Without a shared translation layer, agents hallucinate, leading to decisions being made on bad numbers. The semantic model is the Rosetta Stone for this problem—encoding business definitions, security rules, join logic, and calculation semantics, so autonomous agents can interpret intent probabilistically and retur

AI-powered analytics is everywhere right now. But the payoff? Not so much. Two patterns show up again and again. The first is an “AI everything” backlog that expands faster than teams can deliver. The second is an insights bottleneck that still forces the business to wait in line for basic answers while analysts drown in ad hoc requests. What follows is a practical playbook pulled from how experienced data leaders run their programs: how they prioritize, how they govern, and how they scale access without losing trust. Self-Service Analytics And Data Democratization: The Foundation For Speed And Trust Senior data leaders at high-performing organizations are converging on the same playbook: value-first prioritization, governed self-service analytics, and an AI-native foundation that speeds up decisions instead of simply generating more friction. A quick definition of terms: Self-service analytics means business users can explore trusted data without filing tickets. Data democratization i

Foreign Filing filed 2026-04-15

Your data’s never lived in one place. Customer records might be in your CRM, while sales and operational metrics are split among data platforms. And somewhere, there's critical budget data living in a spreadsheet, owned by a single person on the finance team. Bringing it together has always come at a cost of speed vs. governance. But today, Data Mashups in ThoughtSpot Analyst Studio (currently in Early Access) give analysts and data engineers the ability to blend data from any source into a single, governed model with a single SQL join, in the platform where it will actually be used without waiting on engineering or touching a warehouse schema. For AI-driven organizations, the stakes go beyond just a slow analysis. The Real Problem with Fragmented Data Meaningful analysis requires unified data, and unifying has always been the hard part. You either stitch it manually in a local tool and race against the clock, or you wait on engineering and watch the moment pass while a ticket wor

In the world of corporate travel and expense management, data isn't just a byproduct of business—it’s the lifeblood. Navan empowers over 10,000 global companies to manage billions in annual spend. But when you’re connecting employees to millions of travel options and vendors in real-time, the data complexity is staggering. I recently spoke with Bhuvan Bhatia, Staff Data Engineer at Navan , during our Gartner Data & Analytics Summit session to pull back the curtain on how they’ve revolutionized their financial analytics. Navan’s journey isn't just about faster dashboards; it’s about a fundamental philosophy they call "Trust by Design." The High Stakes of Financial Data Financial analytics at scale is a high-wire act. Navan faces the same challenges and dynamics every day: data complexity with dozens of data sources in multiple formats and intense regulatory pressures such as SOX compliance and GDPR. In this environment, manual errors aren't just an inconveni

In the high-stakes world of retail, the line between market leaders and those playing catch-up is drawn by one thing: real-time business insights. It’s no longer enough to know what happened last week; you need immediate answers to know what’s happening now to drive what happens next. At our recent Spotter for Industries event , I had the pleasure of sitting down with Alex Homan, Lead Data Analyst at Huel—the nutritious (and delicious!), plant-based food company—to discuss how they’ve evolved from a startup of 120 people to a global powerhouse of 350+ employees across the UK, Europe, and the US. For Huel, scaling isn’t just about making more protein bars and meals; it’s about fueling their growth and team with intelligent, accessible data. Here is how Huel is using ThoughtSpot’s AI-powered analytics to fuel every employee with data in real time. 50-60% Reduction in Stakeholder Tickets: How to Move Beyond the Dashboard Bottleneck Before partnering with ThoughtSpot, Huel’s data team face