Most AI consulting stops at a strategy doc. Ours starts in your office.
How AI consulting works at Attu
Every engagement runs in three steps.
Identify. We come to you, watch how work actually gets done, and find the bottlenecks worth automating. A dispatcher will show you in ten minutes what a survey would miss in a month.
Develop. We build against your real data and your real workflows, integrated with the tools you already use — Square, QuickBooks, Jobber, ServiceTitan, whatever you run on. Not off-the-shelf. Not theoretical.
Adopt. We train your team in person, tune the system in production, and step back once it's just how they work. No ticket queues. No handoffs to an offshore team. The people who build it are the people sitting across from you.
Who we work with in Anchorage
We work with Anchorage mid-market operators — general contractors, MEP and electrical firms, freight and logistics companies, healthcare practice groups, property managers, and Native corporation subsidiaries.
If your team loses hours to invoicing, quoting, dispatch, scheduling, or document handling, that's usually the first thing worth building. Read more on what we build under workflow automation.
Why local matters
We're Anchorage-based and every engagement starts face-to-face. That's not a sales line — it's how you find the real bottlenecks. Being in the room is the work.
It also matters that we know how Alaskan operations differ. Dark months change hiring patterns. Text beats email for crews in the field. Per-job equipment trips run more in remote areas than anywhere in the Lower 48. Generic AI playbooks miss that.
"The first team that understood our operations from day one and delivered something our crews could actually use in the field." — Alcan Electrical & Engineering
Questions we get
How is this different from hiring a big AI consultancy?
Big consultancies sell strategy and leave. We build the thing and stay through adoption — that is where most projects die.
How fast do we see something working?
A working pilot in weeks, not months. We scale only what is proven.
What if we've tried AI before and it flopped?
Most failures come from the wrong use case or stopping at the prototype. We pick viable problems and stay until your team owns the result.
See how this looks in practice in our United Freight & Transport case study, or read about AI automation across Alaska.