Gravity

Summary In this episode of the Data Engineering Podcast, Lucas Thelosen and Drew Gilson, co-founders of Gravity, discuss their vision for agentic analytics in the enterprise, enabled by semantic layers and broader context engineering. They share their journey from Looker and Google to building Orion, an AI analyst that combines data semantics with rich business context to deliver trustworthy and actionable insights. Lucas and Drew explain how Orion uses governed, role-specific "custom agents" to drive analysis, recommendations, and proactive preparation for meetings, while maintaining accuracy, lineage transparency, and human-in-the-loop feedback. The conversation covers evolving views on semantic layers, agent memory, retrieval, and operating across messy data, multiple warehouses, and external context like documents and weather. They emphasize the importance of trust, governance, and the path to AI coworkers that act as reliable colleagues. Lucas and Drew also share field stories from public companies where Orion has surfaced board-level issues, accelerated executive prep with last-minute research, and revealed how BI investments are actually used, highlighting a shift from static dashboards to dynamic, dialog-driven decisions. They stress the need for accessible (non-proprietary) models, managing context and technical debt over time, and focusing on business actions - not just metrics - to unlock real ROI. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management If you lead a data team, you know this pain: Every department needs dashboards, reports, custom views, and they all come to you. So you're either the bottleneck slowing everyone down, or you're spending all your time building one-off tools instead of doing actual data work. Retool gives you a way to break that cycle. Their platform lets people build custom apps on your company data—while keeping it all secure. Type a prompt like 'Build me a self-service reporting tool that lets teams query customer metrics from Databricks—and they get a production-ready app with the permissions and governance built in. They can self-serve, and you get your time back. It's data democratization without the chaos. Check out Retool at dataengineeringpodcast.com/retool today and see how other data teams are scaling self-service. Because let's be honest—we all need to Retool how we handle data requests. Your host is Tobias Macey and today I'm interviewing Lucas Thelosen and Drew Gilson about the application of semantic layers to context engineering for agentic analytics Interview Introduction How did you get involved in the area of data management? Can you start by digging into the practical elements of what is involved in the creation and maintenance of a "semantic layer"? How does the semantic layer relate to and differ from the physical schema of a data warehouse? In generative AI and agentic systems the latest term of art is "context engineering". How does a semantic layer factor into the context management for an agentic analyst? What are some of the ways that LLMs/agents can help to populate the semantic layer? What are the cases where you want to guard against hallucinations by keeping a human in the loop? Beyond a physical semantic layer, what are the other elements of context that you rely on for guiding the activities of your agents? What are some utilities that you have found helpful for bootstrapping the structural guidelines for an existing warehouse environment? What are the most interesting, innovative, or unexpected ways that you have seen Orion used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Orion? When is Orion the wrong choice? What do you have planned for the future of Orion? Contact Info LucasLinkedIn DrewLinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links Gravity Orion Looker Semantic Layer dbt LookML Tableau OpenClaw Pareto Distribution The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Lucas Thelosen

Co-Founder

Gravity

Drew Gilson

Co-Founder

Gravity

Summary In this episode of the Data Engineering Podcast Lucas Thelosen and Drew Gilson from Gravity talk about their development of Orion, an autonomous data analyst that bridges the gap between data availability and business decision-making. Lucas and Drew share their backgrounds in data analytics and how their experiences have shaped their approach to leveraging AI for data analysis, emphasizing the potential of AI to democratize data insights and make sophisticated analysis accessible to companies of all sizes. They discuss the technical aspects of Orion, a multi-agent system designed to automate data analysis and provide actionable insights, highlighting the importance of integrating AI into existing workflows with accuracy and trustworthiness in mind. The conversation also explores how AI can free data analysts from routine tasks, enabling them to focus on strategic decision-making and stakeholder management, as they discuss the future of AI in data analytics and its transformative impact on businesses. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details. Your host is Tobias Macey and today I'm interviewing Lucas Thelosen and Drew Gilson about the engineering and impact of building an autonomous data analyst Interview Introduction How did you get involved in the area of data management? Can you describe what Orion is and the story behind it?How do you envision the role of an agentic analyst in an organizational context? There have been several attempts at building LLM-powered data analysis, many of which are essentially a text-to-SQL interface. How have the capabilities and architectural patterns grown in the past ~2 years to enable a more capable system? One of the key success factors for a data analyst is their ability to translate business questions into technical representations. How can an autonomous AI-powered system understand the complex nuance of the business to build effective analyses? Many agentic approaches to analytics require a substantial investment in data architecture, documentation, and semantic models to be effective. What are the gradations of effectiveness for autonomous analytics for companies who are at different points on their journey to technical maturity? Beyond raw capability, there is also a significant need to invest in user experience design for an agentic analyst to be useful. What are the key interaction patterns that you have found to be helpful as you have developed your system? How does the introduction of a system like Orion shift the workload for data teams? Can you describe the overall system design and technical architecture of Orion?How has that changed as you gained further experience and understanding of the problem space? What are the most interesting, innovative, or unexpected ways that you have seen Orion used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Orion? When is Orion/agentic analytics the wrong choice? What do you have planned for the future of Orion? Contact Info LucasLinkedIn DrewLinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links Orion Looker Gravity VBA == Visual Basic for Applications Text-To-SQL One-shot LookML Data Grain LLM As A Judge Google Large Time Series Model The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Lucas Thelosen

Co-Founder

Gravity

Drew Gilson

Co-Founder

Gravity

Kyle Gawley (@kylegawley) was running a high-growth, venture-backed company when he ended up in the hospital partially due to all the pressure he was under. That experience led to some introspection, which I'll ask him about in this episode. We'll also talk about his new company, which he decided to build with a completely different approach to growth.  Follow Kyle on Twitter: https://twitter.com/kylegawley Save three months by trying Gravity's SaaS boilerplates: https://usegravity.app/

Kyle Gawley

Founder

Gravity