A method built around the way these projects actually fail.
Most AI engagements die in the gap between a convincing prototype and something people rely on. Every step below exists to close that gap.
Three stages, each with an exit.
Strategy, readiness, delivery. You can stop after any stage and keep everything produced up to that point. No stage depends on you having committed to the next, and plenty of clients only ever need the strategy.
Work out what is actually worth doing
Most companies do not have an AI problem so much as a prioritisation problem. We go through how the business actually runs and come back with a ranked view of where AI would pay, what each option would take to deliver, and which ideas are best left alone. What you get is a plan specific enough to act on and honest enough to include the things we think you should not build.
How we choose the first project
Get the business ready to use it
Buying tools does not change how a company works; people and process do. This is training built on your own workflows rather than generic examples, the data and integration groundwork that quietly stalls most projects, and clear rules about what staff are and are not allowed to put into these systems. It is the stage most often skipped, and it is the most common reason nothing sticks.
How we bring teams up to speed
Build it, run it, and prove it worked
Whatever the plan calls for (something custom built, a process automated, or software you already pay for finally configured properly), taken all the way into daily use rather than stopping at a prototype that impresses a steering committee. We agree how success will be measured and record where you are starting from, so what you get at the end is a number rather than a story.
What we build and integrateOpinions that shape the work.
The model is the easy part
Frontier models are a commodity you rent by the token. What separates a system that works from a demo that impressed someone is evaluation, error handling, integration, and the operational discipline to keep it accurate as inputs drift.
Scope beats ambition
The single strongest predictor of an AI project reaching production is a narrow initial scope. One workflow, one team, one measurable outcome. Broad transformation programs generate slides; narrow ones generate software.
A baseline or it did not happen
If we do not know how long the current process takes, we cannot claim to have improved it. Baseline capture happens before any code, and it is often the first time a company has measured the process at all.
Trust is lost at the edges
Users do not abandon a system because it is wrong occasionally. They abandon it because it is confidently wrong and gives them no way to tell. Confidence scores, citations, and review queues are what keep a system in daily use.