Tyler Gibbs

Tyler Gibbs

Lead software engineer in applied AI and founder of Grayhaven Industries. I build AI systems, software, and hardware.

I started Grayhaven Industries, an AI deployment company. I believe more abundance in the world requires critical industries that operate at the speed of software. We build custom models and AI agents for healthcare, legal, banking, and utility teams, and we run what we build. So far that includes a model that triages clinical records for a health system and an agent that prepares prior authorizations for a hospital system.

I’m the lead software engineer for applied AI at LexisNexis, where I run technical direction for 20+ production AI workflows in legal research and the LLM infrastructure under them. The things I’ve shipped there that I still think about are Praetor, Gavl, and Meridian: a docket classifier that beat the legacy model, a browser agent for court portals, and a scraper that writes its own scrapers.

Before that, I founded Backwork and led a team of 3. Backwork is now a Grayhaven company. Earlier, I freelanced as an AI engineer for Fortune 500 clients.

I studied CS at Oklahoma State (3.9 GPA), after starting at the University of Oklahoma, where I led AI/ML software for the robotics team.

Some fun facts:

  • I’ve shipped 63 projects since 2018. Some solved a problem. Others were interesting enough to build anyway.
  • I wrote firmware for a robotic dog and the web app that drives it.
  • I trained a forecasting model that beat grid operators in 6 of 7 major U.S. regions with ~40% lower error, then gave it away.
  • I ran 45 experiments training models under simulated radiation up to 10,000x LEO to see what breaks first.

Interests

AI, energy, and robotics are one problem, not three. The limit stopped being algorithms a while ago. It’s power, compute, and heat. A model you can’t power is a demo. A model that can’t move anything is a chatbot.

I’ve trained models, deployed them, written firmware, and built the control software. The physical side is where I keep ending up: energy, machines, the trades. Software mostly skipped that part of the economy, and that’s where the work is now.

I’m most interested in systems that run in production, not in a notebook. Deployment is what tells you which parts of the research were real. The gap between a benchmark and a thing people depend on is where most of the engineering lives, and it’s underrated.

Products

These are the ones I keep working on:

  • Backwork searches Medicare, Medicaid, and commercial payer policies in plain English.
  • Rebar finds, diagnoses, and fixes breakage in production software.
  • Faraday handles estimating, scheduling, time tracking, and invoicing for electrical subcontractors.
  • Tokenbase shows engineering teams how their AI coding agents are used and what they cost.

The rest are on the projects page.

Writing and video

On the blog, I write about what I’ve built. If the subject is attention mechanisms, I show the math. If it’s deployment, I show the code.

I run Applied Tensors, where I explain AI concepts and build things live, and Ares, where I talk about tech without a script.

The books page lists what I’m reading.

Socials

I’m easy to reach. If you’re working on something in AI, energy, or robotics, email me. Email gets the fastest reply.

Last updated: October 2026