The pilot proved nothing
Demos run on clean data and the happy path. Production runs on the exception. Without a baseline measured before you started, no one can honestly say whether it worked.
AI & automation studio · North Texas
AI agents that still work on day 400.
Most AI projects look great in the demo and quietly die three months later. AIGenic designs, builds, and operates AI agents and automation for small and midsize companies, with the baseline measurement, governance, and support model that keeps them running long after the pilot.
25+ years
Enterprise technology, automation, and program leadership
$7.8M
Annual savings and business value from the automation program I built and ran
Up to 60%
Processing-time reduction delivered on automated workflows
4 systems live
Multi-agent platforms designed, built, and running in production right now
The problem
Nobody has a demo problem. They have a day-90 problem.
Three failure modes account for most of the AI work that gets written off. None of them are technical.
Nobody owns it on Monday
The build team leaves. A vendor moves a button, a policy changes, a form gets a field. No runbook, no monitoring, no named owner. Someone quietly goes back to the spreadsheet.
It was never a business case
"We need AI" is not an objective. Cutting quote turnaround from three days to four hours is. Without a number attached, the project has no defense the first time budget gets tight.
What we do
Practical systems you can run, measure, and support.
AI agents & agentic automation
Agents that read, decide, and act inside your real systems, with scoped tool access, guardrails, human approval where a wrong answer is expensive, and a full audit trail.
AI strategy & roadmap
A ranked, costed pipeline of what to automate and in what order, with the business case, the data and access dependencies, and an explicit list of what is not worth doing.
RPA modernization & rescue
Bots that break weekly or cost more than they save. We audit the estate, then stabilize, re-platform, replace, or retire each one, whichever the numbers support.
Process automation & integration
The unglamorous work that pays: intake, quoting, invoice and document handling, reporting, and getting systems that were never meant to talk to each other to do exactly that.
Governance, risk & AI policy
Who may deploy an agent, what data it may touch, how output is reviewed, what gets logged, and what happens when it is wrong. Written to be followed, not framed.
Managed operations
Monitoring, exception handling, tuning, and change management once it is live, either as your operator or as backup for whoever on your team now owns it.
Assessment
Fixed fee, two to four weeks. Process discovery, measured baseline, ranked roadmap, business case. You own the output whether or not we build anything.
Build
Project-based, milestone-billed. Design, build, prove, deploy, hand over. Delivered in increments so value lands before the end date.
Operate
Monthly retainer. Operations, support, tuning, and the reporting that proves it is still worth the line item. Cancellable.
How an engagement runs
Measure first. Build second. Hand it over properly.
Every phase ends with something you can review and a decision you can make, including the decision to stop.
01
Assess & baseline
We map the process the way it actually runs, workarounds included, then measure it. Volume, cycle time, touch time, error rate, cost per transaction. That number is what everything after gets judged against.
02
Design & business case
Target-state design, integration and data dependencies, controls and review points, and a cost benefit model with the assumptions written down where you can argue with them.
03
Build & prove
Built in increments against real data and real exceptions, then run beside the current process so the go decision rests on evidence rather than enthusiasm.
04
Deploy & operate
Phased rollout with a rollback path. Monitoring and alerting live before go-live, not after the first outage. Named owners for the process, the queue, and the technology.
05
Hand over
Source, configuration, prompts, architecture, business logic, and runbooks. Enough for another competent engineer to take it over without calling anyone.
06
Scale & govern
One automation is a project; five is a program. Intake, build standards, risk tiering, and benefit reporting that survives contact with your CFO.
Non-negotiables
Five rules we don't bend.
These exist because breaking them is how automation programs fail. They are written here so you can hold us to them.
- Measure before you build. No baseline, no project. Without one, every savings claim afterward is a guess wearing a suit.
- Humans stay in the loop where a wrong answer is expensive. Review points are set by consequence, not by how confident the model sounds.
- Everything is documented and handed over. Source, config, prompts, architecture, logic. You own it outright.
- Fix the process before automating it. Automating a broken process produces broken output faster, at higher volume, with less oversight.
- We tell you when the answer is no. If the payback doesn't justify the build, you hear that, even when it costs us the work.
Proof, not promises
Four systems running in production. The architecture is published.
Most AI consultancies ask you to take the capability on faith. These are described in enough detail that you can judge the engineering before you spend anything.
01
Feasibility agent
Mines public GIS to build off-market target lists, then models subdivision feasibility. Backtested against 33 closed transactions, which is how we found the first model was off by 3.35×.
Read the architecture02
Signal-driven targeting
Watches a defined market for the events that create an opening and returns four companies each morning, with who to call, why today, and every score opened to its reasoning.
Read the architecture03
Multi-agent compliance workflow
Daily discovery, deduplication, weighted scoring against a published model, and an auditable log for a regulatory requirement, with hard boundaries written into the spec.
Read the architecture04
Governed evidence base
610 verified sources in Postgres with provenance tagging, a single sanctioned write path, and integrity confirmed three independent ways.
Read the architectureAbout
Brian E. Smith
Founder, AIGenic Technologies, LLC · Van Alstyne, Texas
Most people selling AI arrived in the last eighteen months. I came from the other direction. Twenty-five years building and running the systems AI now has to plug into, then five years running an enterprise automation program that had to prove its savings to a CFO every quarter.
I built that program's Center of Excellence from nothing: intake, prioritization, governance, build standards, compliance, value reporting. Then I led the team that ran it. $7.8M in annual savings and business value, with processing times cut by as much as 60%. Most of that work was in insurance and healthcare, which teaches you things unregulated industries don't: that an audit trail isn't optional, that "the model was confident" is not a defense, and that a control nobody can follow is worse than no control at all.
Building an agent is the easy half. The hard half is the exception queue, the audit trail, and the person who owns it on Monday.
The last stretch of that work was spent evaluating whether agentic automation was ready for production. It was. Almost nobody was packaging it for companies below enterprise scale, so I left to do that.
Industries
- Insurance: underwriting, claims, licensing, Medicare
- Healthcare: hospital systems, imaging, research infrastructure
- Financial services: credit and data environments
- Real estate and land development
- Gaming
- Marketing and professional services
- Public sector, technology, and telecom
More on the engineering, and the four systems above, on the work page.
Contact
Thirty minutes. No pitch deck.
Bring the process that's costing you the most: the one people complain about, or the one that quietly eats a person's week. Rough numbers are fine. Nobody expects you to have measured it yet.
What happens next
- A reply within one business day. From me, not an autoresponder sequence.
- A 30-minute call. You describe the process; I ask about volume, exceptions, systems, and who owns it today.
- A straight answer. Either a scoped proposal with a number, or a recommendation to do something else, including nothing.