Systems | Development | Analytics | API | Testing

It's the day before your POC, and the embedded analytics demo still looks like it belongs to someone else. Your designer handed over a brand guide last week. Your developer has been buried in CSS variables ever since: cross-referencing token names, mapping changes across components, reloading the page after every tweak to see what broke. The UI is almost right. The nav color is close. The typography still isn't matching, but there's no time left.

Your data engineers have spent months getting your metric definitions right: revenue recognized the way finance approved it, churn calculated the way your exec team aligned on it, and pipeline logic that your rev ops team actually agrees on. And then a new tool arrives, and someone has to do it all again.

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.

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.

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.

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.

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.

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.

Every analyst has been there: Deadline looming, data in hand, and a BI tool that either requires a workflow you haven't learned or a colleague you can't reach. So you open Excel. It's familiar, it's flexible, and it works right now. So that's where the work happens—and now where insight stays, ungoverned and invisible to your team, your analytics stack, and your agents.

We launched Spotter with one goal: give every enterprise team their own analyst—an agent that reasons through business complexity, validates its own outputs, and surfaces answers you can actually act on. The response from customers made one thing clear: the ThoughtSpot foundation works. Teams trust Spotter, because it doesn’t only rely on an LLM to reconstruct your business logic on the fly—a process that produces different answers depending on how a question is phrased.