AI consulting in Orange County

Most of the job is subtraction. A company shows up with twelve things AI could theoretically do for it. The work is picking the one workflow where the math is biggest and the test for "did it work" is writable, then shelving the other eleven.

After that it turns into ordinary engineering. Where does the data live. Who touches it. What does a correct answer look like, and how will anyone know we got one.

Here the consulting half and the building half are the same engineers. That's deliberate. A recommendation that can't survive contact with your codebase isn't worth the deck it's printed on.

Companies in Irvine, Newport Beach, Costa Mesa, and Santa Ana get an in-person version of this. Everyone else gets the identical thing over video.

Because the pilot was never tied to a number. MIT's NANDA report found that 95% of enterprise AI pilots deliver no measurable P&L return, and the Institute of Project Management puts 88% of them as never reaching production at all. Those are mostly not technology failures. A model that drafts contract summaries works fine in a demo.

It stalls because nobody wrote down what "working" meant in dollars before the demo, so there is nothing to point at when the budget meeting arrives. Gartner attributes 85% of failed AI projects to data quality. IDC finds 65% of companies name data readiness as the blocker. Both are boring problems, and boring problems get solved before you touch a model.

The fix is to shrink the pilot until one person's week visibly changes. Read the AI Pilot Autopsy if yours is already stuck.

Builds are priced at 10 to 20 percent of the measured annual value, never by the hour. That number comes out of discovery, so by the time anyone quotes a build, both sides are looking at the same arithmetic. Every build carries the same promise: we work it until it passes your acceptance test.

You own the code outright. It is never resold, and there is no license to renew.

Replies inside two business days, from Jason, not a sales queue.

Five steps, in this order, and step one costs nothing.

1. Autopsy. Email us what stalled. You get two pages back on why, and the two changes that unstick it. Free and ungated. If you don't reply, we don't chase you.
2. Discovery. $2,500, about two weeks. We sit with the workflow, count what it actually costs you per year, and write down the number.
3. Acceptance test. Before any code exists, we agree in writing on what "done" looks like. Specific inputs. Specific outputs. A threshold you can check without us in the room.
4. Build. Priced at 10 to 20 percent of that measured annual value. It runs until it passes the test we already agreed on.
5. Own it. The repository, the infrastructure, and the credentials are yours. Keep us for operations or don't.

Any step can be the last one. Stopping after discovery and building it yourself is a fine outcome, and one reason the documents are written to be handed to somebody else.

Our team of creative engineers and systems engineers, led by principal Jason Portillo. Senior people, and the same ones who write the code. Nobody hands your account to a junior consultant after the sale.

We take a deliberately small number of engagements at a time. If we're full, you get a start date instead of a slot, and we say that on the first call rather than after you've signed something. It's a qualifier, not an apology. It keeps the attention on the workflow that pays.

The engineers who map the workflow are the engineers who build it. Nothing gets lost in a handoff document, because the work never gets thrown over a wall to a different group.

More on how we work: the FAQ.

Each of these is genuinely good at something. The question is what it costs you when the fit is wrong.

Where this practice fits: senior engineers, a quantified target, and code you own. If what you need is board-level air cover and a change-management program across thousands of employees, that's the first row above. If it's custom software that has to be right, that's this.

Yes. The practice is based in Orange County, California and the work is remote-friendly across the United States. Most of a build happens in a repository and a video call, and that part doesn't care where you sit.

What being local buys you is the first two weeks. Discovery goes faster when we can stand in the room while somebody does the job we're about to measure, and companies in Irvine, Tustin, Anaheim, and Huntington Beach get that at no extra cost. Out of state, the arrangement is one on-site week and the rest remote.

Industry matters less than you'd think. The pattern is the same whether the workflow lives in a claims desk, a shop floor, or a clinic. Worth noting: healthcare has the lowest pilot-to-production rate of any sector, at 8%, so scope there gets cut harder.

1. MIT NANDA report (2025), 95% of enterprise AI pilots deliver no measurable P&L return — softwareseni.com
2. 88% of enterprise AI pilots never reach production — institutepm.com
3. Gartner 85% data-quality failure rate; IDC 65% data readiness — pertamapartners.com
4. Healthcare pilot-to-production rate of 8% — softwareseni.com
5. No-code builds rewritten in custom code within two years, 25–30% — thebrightbyte.com
6. Offshore total cost of ownership 25–150% over headline rate; QA rework 15–26% of hours — smartdev.com
7. In-house AI team at $1M–1.8M loaded in year one, 3–6 month ramp — groovyweb.co