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We build, integrate and teach AI for small businesses in Southern Utah — and run a platform that turns the reports you already export into plain answers about what happened and what to do next.
What we actually think
What you see in a demo is a model doing one task, once, in clean conditions, with a skilled person steering it. What your business needs is that task done four hundred times, on inputs that arrive misspelled and half-empty, at six in the morning, with nobody watching.
Those are different engineering problems, and the second one is where nearly every small-business AI project quietly dies. The question worth asking a vendor isn't whether it works. It's what happens on the run where it doesn't.
Your staff are smarter than the model. What they are not is available at eleven at night, identical on the four hundredth repetition, and willing to read a four-thousand-row export line by line without their eyes sliding off it.
Point these tools at the work that rewards being tireless. Keep your people on the work that rewards judgment.In most small businesses the information is scattered across a booking system, two spreadsheets, an inbox, and one person's memory. No model fixes that. Ask a brilliant assistant a question about numbers it can't see and you get a confident, useless answer.
Getting that information into one place, in a shape something can reason over, is the unglamorous part. It is also most of the job, and it's the part that keeps paying after the novelty wears off.
One task. Running in weeks, not quarters. Scoped tightly enough that you can tell whether it actually worked before deciding to spend more.
If it holds, we widen it. If it doesn't, you've lost a few weeks instead of a year and a platform contract — and you've learned something specific about your own operation either way.
All pointed at the same outcome: less time on repeat work, and better answers out of the information you already have.
Training your team so the tools keep being useful after we leave. Taught against your own work, not generic examples.
The tasks eating your week, handed to something that does them the same way every time and tells you when it can't.
A portal your business logs into. It reads the reports you already export and writes what actually happened.
Your pricing, policies and service menus in one place your team can ask questions of — answered from your documents, not the open internet.
When nothing off the shelf fits how you work, we build it. Yours — not a template with your logo dropped on it.
Booking software, spreadsheets, email, CRM. AI is only worth anything where your business already lives.
No integration to configure and no access to your booking system to hand over. You run the sales summary you already run, and drop the file in.
Several at once is fine, and one bad file doesn't stop the rest. The raw rows are stored exactly as they arrived — so if a column was mapped wrongly, it gets corrected on our side without anyone asking you to export again.
Totals are computed in the database rather than by a model, because arithmetic should be arithmetic. That makes every figure inspectable and repeatable.
Then it reconciles two independent exports against each other and reports the gap. Twenty-seven files in, the daily numbers landed within 2.2% of the monthly total — and the difference resolved to five Sundays when the business was shut.
Only once the numbers are settled does a model get involved, and only to explain figures it has been handed. It cannot invent one.
Two to four findings a month, each ending in something you could do that week, each recording the metrics it was drawn from. That's how a claim like "package refunds ran six times sales" stays checkable rather than impressive.
Most reporting hands you a total and asks you to trust it. Ours shows the working, then reconciles it against a second export out of your own system.
Two independent exports, arriving by different routes, agreeing to the cent. When they don't agree, the portal shows the gap and names the reason rather than picking a winner.
Running against a personal care business in St. George. Real exports, real revenue, real analysis — not a demo dataset.
You're trusting us with revenue figures. It's fair to ask where they sit and who can reach them.
Docker, Postgres 17 and nginx on hardware we control. No per-seat platform fee, and no third party holding a client's revenue figures as a condition of using the product.
Row-level security on every table. One business's data isn't merely hidden from another account in the interface — it isn't returned by the database at all.
The automation server that moves data around is never exposed to the internet. It sits behind an identity check at the network edge, before a request reaches it.
The model receives computed figures and is prevented from producing numbers of its own. Every published finding carries the metric IDs it was built from.
Nightly database backups verified by inspecting their contents, not by checking that a file exists and is roughly the right size. A backup you haven't opened isn't a backup.
Where two sources describe the same period, both are kept and compared. Agreement is evidence; disagreement is surfaced with its explanation attached.
Access control that's only been tested through the interface hasn't been tested. We verify it the way an attacker would: by making a request to the database directly, with no credentials attached, and confirming it comes back empty. Passing that test is the difference between data that's hidden and data that's genuinely unreachable.
An axiom is something you build on because it holds. That's the standard we hold the work to.
Reliable systems. Secure, scalable, and still running in three years without someone babysitting them.
Solutions derived from how your business actually works — not from whatever happens to be trending this quarter.
We start with what's genuinely costing you hours, not with what demos well in a meeting.
Built in Southern Utah, for businesses we can drive to. You get a person, not a ticket number.
Everyone is selling AI right now. These are the three things we've decided not to do, and they're the reason the rest can be trusted.
Some problems are a spreadsheet and a better process. We'll say so when that's the answer, even though it's the smaller invoice.
Where there isn't data to support a claim, our tools say so rather than producing a confident number. No forecast until the history earns it.
When two sources disagree, we show you the gap and the reason. Daily files landed within 2.2% of the monthly total — the missing days were Sundays, when the business is closed.
Tell us what's eating your week. If there's something worth building, we'll tell you what it is and roughly what it takes. If there isn't, we'll tell you that too.