Book a Call

AI consulting: we build the system around the model

Model access is free and you already have it. What you don't have is the data plumbing that feeds a model your actual work-order completions, the guardrails on what it's allowed to change, the human approval step that catches it being wrong before anything moves, and the integration into the system where the work actually happens. That's the part that takes engineering, and it's the part that decides whether the thing still runs in eighteen months.

READ ONLY
The model is the smallest and plainest thing here, and the engineering around it is the work. Its feed from the system of record is read-only, nothing it drafts moves until a person at the checkpoint approves it or rewrites it, and what leaves the desk goes back to the ticket it came from.
Model Data plumbing Actual work-order completions Guardrails What it can change Human approval step Catches it being wrong first System-of-record integration
The model sits at the center of data plumbing, guardrails, a human approval step and system-of-record integration, the four things that take engineering.

Where AI goes in your stack

We build AI as part of the system of record, reading its tables under the same permissions as everyone else who touches it, instead of bolting a chat window onto the side of it. We already run the system your business runs on. For manufacturers, that's NetSuite. For dental groups, it's Denticon, Dentrix Enterprise, or the Microsoft Fabric layer we've built to sit above them. A model that only sees what you paste into a chat window can't touch your work-order completions or your production schedule. A model built into the system where that data already lives can.

What we've shipped

The clearest evidence of how we build is the ticket-triage agent we run against our own helpdesk. Not a client's queue — ours. It reads each incoming ticket, drafts a fix or automation plan grounded in our NetSuite knowledge base, and posts it as a private note that one of our engineers approves or rewrites. It holds a read-only NetSuite role, so it can read the account it reasons about and cannot change it, and work in flight survives a restart instead of quietly disappearing. We run it on ourselves every day, which means when it drafts something wrong, we are the ones who wear it. Read how it works, and what happens when it's wrong.

Choosing what to build

Not every AI idea is worth your engineering budget. We can tell you which of these holds up and which gets abandoned in a year because we have done both to ourselves: one agent that stuck and now runs every day, and an idea of our own we stopped building once we saw what it would cost to keep running. See how we weigh build vs. buy.

Start with a specific one

This conversation is more useful with a system and a task in it than with a strategy. Tell us where the work sits today — NetSuite, a practice-management system, a shared inbox — and what you would let an agent change on its own. We'll tell you whether it is worth building, and what we would need to see in your data first. Send us the details, or book a call if it's quicker to talk it through.