From gigabytes of robot telemetry per minute to natural language search across multimodal data, Alloy is tackling one of the most underappreciated bottlenecks in robotics: making sense of what robots are actually doing in the real world.

Joe Harris, founder of Alloy, joins Greg to unpack how his background in electrical engineering, machine learning, and growth teams shaped a product that helps robotics companies move faster, ship more reliably, and avoid rebuilding the same internal tooling over and over again. What started as frustration with inaccessible data and slow feedback loops has become a platform designed to turn robot data into a shared, searchable source of truth across engineering, validation, and commercial teams.

The conversation dives deep into why replay tools break down at scale, how modern LLMs are changing what’s possible with robotics telemetry, and why deciding what not to build is one of the most important skills for early-stage founders.

Highlights:

• Joe’s path from electrical engineering and machine learning research into growth teams at scale, and how feedback loops became a unifying theme across software and robotics
• Why robotics companies are drowning in data but starving for insight, with robots generating gigabytes per minute across video, sensor data, and logs
• How Alloy helps teams move beyond one-off replay by enabling cross-sectional analysis, natural language search, and summarized field test reports
• The validation and verification teams who feel the value first, and how faster analysis turns into faster deployments for customers
• Why most robotics startups should not build their own telemetry and analysis stack, and how the industry is entering a tooling renaissance similar to early cloud software
• The importance of pain times frequency when deciding what features to build and what to cut
• Lessons from early mistakes, including why free pilots often fail and how paid pilots create real commitment on both sides
• Joe’s philosophy on early hiring, small teams, mission alignment, and building culture without unnecessary process
• What the next 12 to 18 months look like for Alloy as robotics fleets scale and foundation models reshape the landscape
• A long-term vision for a world of abundant automation, where robots learn continuously from experience and data interpretation becomes critical infrastructure

If you are building robots, deploying them at scale, or thinking about the unseen infrastructure required to make robotics reliable in the real world, this episode offers a candid and deeply technical look at what it takes to turn raw robot data into real-world progress.

Learn more about Alloy: www.usealloy.ai

Connect with Joe Harris: https://x.com/_joe_harris_

Connect with Greg Toroosian: https://www.linkedin.com/in/gregtoroosian