AI advice from people who ship it.

AI consulting has a credibility problem, and it is deserved. The standard engagement produces a maturity model, a list of use cases copied from a vendor deck, and no number anyone can be held to. Six months later nothing has shipped and the budget is gone.

We start from the work instead of from the technology. What does your team actually do, how many hours does each of those things cost, how often does it go wrong, and how bad is it when it does. Only then do we ask which of them a model can do well enough today, because that question is meaningless without the first four answers.

The output is a costed inventory, a scored shortlist, a payback estimate for the top candidate with its assumptions written down beside it, and a 90-day sequence. It regularly recommends buying a tool, or doing nothing in a given area yet. A roadmap that only ever says build is a sales document.

We can advise on this because we run it. Our own business is operated by an agent fleet in production, and we publish both what it saves us and what has broken. If you already know what you want built, our AI agent development line takes one bounded workflow at a fixed price, and AI workflow automation is the operations layer that runs it afterwards. The shortlist usually maps to one of the eleven AI systems we build, each described with its build steps and price.

We serve the US and Canada from downtown Vancouver. If you are local, the Vancouver page covers in-person kickoffs and how we build under PIPEDA and BC PIPA. Canadian companies funding this work should read how the BDC LIFT program works.

Everything is published: the audit, the pilot build and the retainer are all listed on the pricing page.