Systems | Development | Analytics | API | Testing

From Dashboards to AI Agents: Huel's Analytics Journey

You’ve rolled out a modern data stack, built self-service dashboards, and empowered your team to ask their own questions. Job done, right? Not quite. The data landscape is shifting rapidly beneath our feet, which makes it critical to understand how to build your AI for BI platform so you can scale and navigate technology evolutions.

ThoughtSpot + ClickHouse Delivers Agentic Analytics at Scale

Agentic analytics, embedded customer-facing reporting, and everyday business metrics now demand the same thing: performance at massive scale, and answers fast enough that a business user never notices the wait. Most generic databases were never designed for that combination. They assume a small population of analysts writing SQL and query latency measured in seconds, not AI agents and business users asking questions around the clock.

Which AI Analyst Holds Up Best for Your Hard Questions?

Analytics vendors claim their AI answers questions accurately, but almost none of them will show you how they checked. The standard move is a percentage with no denominator: "90%+ accuracy on internal benchmarks." No dataset you can download. No scoring method you can inspect. No competitor runs under the same conditions. You're asked to trust the grade without ever seeing the exam1 We ran the exam in public terms instead.

[AgentSpot Showcase Series] Using AgentSpot to Automate Weekly Customer Status Updates

Every week, ThoughtSpot Engagement Manager MJ Densmore used to spend up to an hour per customer manually pulling notes, digging through Slack, and checking Salesforce — just to write a status update. Now, AgentSpot does it all automatically, pulling from meeting notes, Slack messages, and support cases to generate a concise summary she can review and send in minutes. Watch to see how a quick, single prompt does the work of a full hour.

[Finance Demo] AgentSpot Use Case - Procurement Automation: PR Approvals & Virtual Card Management

The procurement team at ThoughtSpot built two AgentSpot agents to tackle manual, time-consuming workflows — and the results speak for themselves. Agent 1 – Coupa PR Approval Nudger: Automated purchase requisition reminders that once took hours of manual chasing in Coupa and Slack. Approval cycle time dropped from 6.62 days to 2.8 days. Agent 2 – Virtual Card Intelligence: Cross-validates PO and requisition data against Spend Flow to surface budget leakage, flag expiring cards, and send personalized Slack outreach to card owners. All without manual audits.

[Finance Demo] - AgentSpot Use Case - Virtual Card Spend Oversight

Manually cross-validating virtual card data across disconnected systems was a constant pain, until the procurement team built an AgentSpot agent to do it automatically. This video showcases a Virtual Card Intelligence agent that pulls PO and requisition data from Google Drive, cross-validates it against Spend Flow, flags expiring cards and utilization risks across 30/60/90 day bands, and sends personalized Slack outreach to card owners. Plus a full 9-tab Excel report covering KPIs, expiry, utilization, and reconciliation. The best part? This is all done in just 4-5 minutes.

SpotterCode in the Visual Embed Playground: Faster Embedding, No Setup Hassle

Embedding ThoughtSpot into your application just got dramatically faster. SpotterCode is now in the Visual Embed Playground. Ask in plain language (rename an agent, apply a custom theme, configure an embed component), and SpotterCode generates exactly the code you need, right inside the ThoughtSpot app. In this video, see how SpotterCode eliminates the setup friction that slows down every embedded analytics build and what it looks like to go from question to working code in seconds.

Put Your Data to Work with ThoughtSpot in ChatGPT Work

Every organization says it wants self-service analytics, but very few have it. What actually stalls true self-service analytics is the invisible work that has to happen before anyone can ask a data question: the semantic layer, query engine, security model, and analytics app. That work lands on your product and data teams, who are already time-strapped to deliver core features and strategy, and it lands on them again every time a new question, a new tenant, or a new dashboard request arrives.