AI consulting from people who ship the thing
We'll tell you which AI investments hold up and which get quietly abandoned in a year. That judgment is worth something because we've done both to ourselves: one agent that earned its place and now runs against our own helpdesk every day, and an idea of our own we stopped building once we saw what it would cost to keep running. We don't broker introductions, and we don't take a margin on someone else's work.
Build vs. buy
Most of what gets called an "AI strategy" is a build-vs-buy decision wearing a costume. AI features are shipping inside NetSuite, inside your practice-management system, inside the tools you already pay for. Sometimes the vendor's version is good enough, and building your own would just be reinventing a worse copy of it. Sometimes it isn't, because the vendor's version can't reach your work-order completions, your patient scheduling data, or whatever system of record actually holds the information the decision depends on. That line is where most of our advisory time goes. We'll walk your case down it — what the vendor's version can reach, what it can't, and what closing the gap would cost to build and keep running — before you spend the budget.
Where AI pays off — and where a SQL view already would
Not every problem pitched as an AI problem is one. A lot of the requests we hear described as "can we get AI to summarize or flag or predict this" are answered faster and more reliably by a SQL view, a scheduled report, or a rule someone can actually audit. AI earns its cost when the input is too messy for rules to hold up — free-text tickets, inconsistent vendor descriptions, judgment calls that used to live in one person's head. We'll tell you when a report would have done the job and when the input really is messy enough to need a model, because building the wrong one wastes the same budget either way.
Data readiness is the actual blocker
The reason most AI projects stall isn't the model. It's that the data the model would need — clean, consistent, in one place, tied to the system of record — doesn't exist yet. Most of our engagements spend real time getting NetSuite, Denticon, Dentrix Enterprise or Fabric data into a state where anything can be built reliably on top of it. That work has to happen before an AI feature can, and it is usually the actual project, not a preamble to it.
Governance
An agent that can act needs limits on what it's allowed to touch and a record of what it did and why. We build those in from the start rather than documenting them afterwards. The read-only defaults and the human approval step on anything that changes a system of record are not a policy position — they are the guardrails running in our own agent, described on the agents page. The audit trail gets built per system, against whatever that system can actually be made to record.
Have the argument before you spend the budget
This one works better as a conversation than as a proposal. Bring the AI idea someone in your organization is pushing for, and we'll take it down the same line we take our own: what your system of record can actually feed it, whether a report would answer the question, and what it costs to run in year two. Book a call, or write to us if you'd rather put it down in writing first.