Omni

Have you ever sat through same boring eCommerce demo? I know I have. I've given it a billion times and was ready for something new. But how was I going to change that same old demo? I would have to build a fake demo dataset, transform that data in dbt Labs, model and create content in Omni, and build a fully functional embedded web application. My skill set only covers one of those four. This is where Cursor came in. Using Cursor I got to moonlight as a data engineer, analytics engineer and a software engineer in the same IDE. Check out my blog below walking you through how I used Cursor, dbt and Omni to build a fully functional AI powered data application for a fictional SaaS company, Data Blobby, that our SE teams could demo to prospects. Omni is excited to be partnering with SpaceX Super Intelligence in their Partner Network launch! See how he built it 👉 https://lnkd.in/g-4zSkf7

Omni is a launch partner in the SpaceX Super Intelligence Partner Network 🤝 We love building in Cursor, and so do many of our customers. Omni's Cursor plugin and MCP server bring governed data into those workflows, so you can query, define new metrics, and build data apps on the shared definitions your team trusts without leaving your preferred workspace 🛠️ Here's what that looks like end to end. Peter on our Solutions Engineering team built a demo customer portal for our sales team, starting from one paragraph in Cursor and ending with embedded dashboards and AI chat 👉 https://lnkd.in/g3u9RfAH Thanks to the SpaceX team for the partnership, and check out this blog from Peter for a deeper dive on Omni + Cursor.

If you read nothing else: Omni will soon support 100% custom data viz. That means if you can describe it, our AI can build it, and it runs on the definitions in your semantic layer. I’ve spent over 20 years working on data visualization software. Data visualization sits at the intersection of computer science, design, and data analysis. Because chart libraries are usually consumed by highly opinionated viewers, building a single chart that satisfies everyone is already difficult. In BI, coupling chart building to a generic tool is even harder - your library must handle data shapes it hasn't seen before. To use a video game analogy: this isn't hard mode, it's nightmare mode. You can’t make everyone happy all the time, so eventually you need a custom data viz escape hatch. That’s exactly what we’re building. Want a bespoke version of classic cartesian charts? Now you can have it. Want to create a one-off data viz to tell a story for your report? Go for it. Want to replicate your CEO’s favorite Economist chart aesthetic? Yep, you can do that too. Of course, we’re coupling this to Blobby, our AI agent. We're also making it available through our MCP connectors so you can use AI to build whatever you want, wherever you want. All of this stays backed by our governed data model, ensuring the right data is visualized for just the right viewers.

This Thursday I'm joining Chris Merrick (CTO, Omni) to talk about how BambooHR went from decision to GA on Elite Analytics, our branded AI data product, in four months, and has since grown it to 100K users. Chris and the Omni team have been amazing partners. Come hear what worked, what we learned, and how we did it without pulling engineering off our core roadmap. 📅 Thursday, Oct 22 · 10am PT / 1pm ET 🔗 Register: https://luma.com/94qpxqrs

Users want to build in Claude. It’s fast, it looks great, and it’s where the rest of their work already happens. Data teams want to know it’s right - or at least that the answer is interpretable, correctable, and defensible when an exec inevitably asks “how did we get this number?” Good news: you can have both. Tell Claude what you’re trying to build, and it builds an Omni app right there in the conversation - live data you can touch, explore, and filter. Every app comes with a link back to Omni, so it’s: → Shareable instantly → Governed - revenue means the same thing it means on every dashboard, and each viewer only sees what their permissions allow → Interpretable - your data team can open it, inspect how it was built, and fix it → Always current - it updates automatically when the semantic model changes Build where you work. Trust what you build.

If you’re evaluating or rolling out Omni (or any AI-native BI tool) and aren’t sure how to get consistent, trustworthy AI answers on your data, this one’s for you. Ask “show me revenue” five slightly different ways and you can get five different answers. Adding layers of context to your semantic model fixes that. Short version: https://lnkd.in/gKQDUN5n Link to hands on workshop in comments.