AI consulting in Longview for mid-market ops, built by a local engineer.

Longview has always been a town that makes things. The city that boomed on the East Texas Oil Field now runs on Eastman Chemical's complex, Komatsu's mining-equipment plant, Trinity Rail, and the million-square-foot distribution warehouses going up along I-20. Around those anchors sit the industrial contractors, fabricators, and energy services companies that supply them, plus two hospital systems and the regional financial institutions and professional firms that serve all of it.

Companies at that scale have the operational problems that AI is genuinely good at: high-volume document handling, order-to-cash processes stretched across systems that were never designed to talk, field data that arrives as scanned PDFs and gets keyed in by hand. And much worse access to senior engineering help than their counterparts in Dallas. The national firms will sell you an engagement and staff it with consultants two years out of school. The local IT shops keep your network running but don't build production AI systems. That gap is the entire reason this business exists.

Longview is full of people who work on serious industrial equipment. You already know the difference between built-to-spec and bolted-together, and you know which one is still running in year three.

I'm Byron Walker, a Staff Site Reliability Engineer with thirty years in technology and a decade spent keeping large enterprise systems online. That background is the whole argument. Most AI projects don't fail at the demo. They fail at 2 a.m. three months later, when a model update shifts behavior or an upstream schema changes and nothing throws an exception. I build AI systems the way production infrastructure gets built: instrumented, independently verified, and honest about what they got wrong. Then I operate them, because a system nobody maintains is a system quietly going out of date.

Roughly 50 to 500 employees, and smaller operations through the $1,500 one-week automation sprint. Manufacturers and fabricators where quoting, POs, and job documentation move between systems by hand. Distribution and logistics operations along the I-20 corridor with order-to-cash processes that consume more people than anyone has counted. Oilfield and energy services companies whose field data arrives as photographs of paper. Healthcare administration groups buried in credentialing paperwork, vendor invoicing, and supply-chain documents, meaning the non-clinical side; patient data stays out of scope by design. Regional financial institutions with compliance documentation where accuracy isn't negotiable.

The common thread isn't industry. It's a process that's expensive, high-volume, currently done by people doing the same thing repeatedly, and important enough that getting it quietly wrong would be a serious problem. That last part is why verification architecture is the center of how I build rather than a feature bolted on at the end.

Nothing I build fails silently. You get a monthly report of what ran, what escalated, and what it got wrong, with the receipts. The wrong-answer section is in every report, including the clean months.

Everything ships in your accounts, in your name, with documentation and team handoff at delivery. No proprietary runtime, no black-box middleware. If you cancel the retainer, the system keeps running and any competent engineer can pick it up. That's a deliberate design constraint, not an accident.

Start with the recorded demos: agent fleets working a steel order desk and an oilfield billing chain end to end. Real model calls, real costs, a written answer key the fleet never sees, and every miss published next to the successes. See the standard every system I build reports against on the reliability page, a working demonstration of the verification architecture, including a button that injects a silent failure so you can watch it get caught. The incident platform case study covers how agents with real authority get built and verified, and engagement pricing is published in the open.