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

Tyler is a real regional economy, not a suburb of one. Two large hospital systems anchor a healthcare sector that employs thousands and generates administrative volume to match. Around them sit the manufacturers, distributors, energy services companies, regional financial institutions, and professional firms that make a city of a hundred thousand function as the commercial center for all of East Texas.

Companies at that scale have the same operational problems as their counterparts in Dallas: document volume nobody can keep up with, three systems that don't talk to each other, a process that quietly consumes two and a half people. And considerably worse access to senior engineering help. The national firms will sell you an engagement staffed by consultants two years out of school. The local IT shops don't build production AI systems. That gap is the entire reason this business exists.

You get the senior engineer at every phase, including the two the national firms hand to someone else: the build and the operate.

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 Then I operate them, because a system nobody maintains is a system quietly going out of date.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 distributors with order-to-cash processes spanning systems that were never designed to talk. Healthcare administration groups drowning in credentialing paperwork, vendor invoicing, and supply-chain documents, meaning the non-clinical side; patient data stays out of scope by design. Energy services companies with field data that arrives as PDFs. Regional financial institutions with compliance documentation requirements that make accuracy non-negotiable. Professional firms where intake and document review consume billable capacity.

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 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.