Building the Data Brain for AI Agents: Toby Mather, Co-Founder of Rig, on Data Infrastructure, Enterprise Sales and the Agentic Future

What happens when companies deploy AI agents before fixing the data those agents rely on?

In this episode of Riding Unicorns, James and Hector sit down with Toby Mather, Co-Founder of Rig, the AI-native data infrastructure company building the data layer that allows humans and AI agents to work from the same trusted context.

Rig started as a "data brain" sitting on top of platforms like Snowflake and BigQuery, making sense of fragmented company data so teams could use tools like Claude and Cursor to build applications, automations and analytics. It has since expanded into an end-to-end data stack covering ingestion, warehousing and context.

Toby explains why LLMs alone aren't the answer to enterprise data. While language models are excellent at understanding context, businesses still need deterministic systems like SQL and structured tables when they want accurate answers from their data. Rig's thesis is that AI should make sense of the complexity, but the underlying calculations still need to be grounded in reliable data.

The conversation also explores the rush towards autonomous AI agents. Toby argues that many companies are trying to run before they can walk: connecting agents to dozens of fragmented systems without first creating the clean, consistent data infrastructure they need. Get the context right, and the journey from human-in-the-loop to autonomous agents becomes much easier.

Topics Covered

• Why AI agents need better data infrastructure
• Building a "data brain" for humans and AI agents
• Why LLMs aren't always good at data reasoning
• Why 55-year-old SQL and tables still matter in the AI era
• Moving from human-in-the-loop to autonomous agents
• How Rig uses Rig to run its own business
• Building agentic workflows across CRM and customer success
• Toby's lessons from building and selling Lingumi
• Why founders should choose enormous markets
• Landing major enterprise customers as an early-stage startup
• The PULL framework for B2B sales
• How to sell enterprise software without becoming a services business
• Competing with incumbents like Snowflake and Databricks
• Balancing customer feedback with founder-led product intuition
• Why agentic customer success could be a major opportunity

Toby also shares some of the more surprising ways customers are already using Rig, from identifying overpayments hidden across thousands of invoices to building AI-powered customer success systems that identify problems and proactively contact customers.

This is a conversation about the infrastructure beneath the AI agent revolution: getting company data into the right shape, giving AI the context it needs and building the foundations required for truly autonomous work.