Task breakdown
We look at what already runs, where the bottleneck is and what you would count as a result. We separate what would be nice from what would pay off.
01 / DESIGN
We work out the economics before the first line of code. Half of the AI requests we see are solved simpler and cheaper, and that is best known at the start.
Most failed AI projects did not fail in development. They failed the moment a solution was chosen before the problem was understood: a fashionable stack, a good demo, and then operating costs at real volume that outran the benefit.
We start from the other end. First we calculate what one request costs and what the whole system costs per month at your load. Then we decide whether AI belongs here at all.
We look at what already runs, where the bottleneck is and what you would count as a result. We separate what would be nice from what would pay off.
A diagram of components that can be maintained: where the model sits, where plain code does the job, where the database is, and what happens when each part fails.
Cost per request and per month of operation at your load. Development is priced separately from running costs.
If the task is solved by a procedure, a script or plain automation, we say so and explain why.
We run our own gateway to language models with cost-based routing: some tasks go to cheap models, some to local ones, and only quality-sensitive tasks reach the expensive ones. That split is a design decision, made before any code, and it saves the largest share of the budget.
If the task is not for us, we say so in the first conversation and, where we can, point you to someone who fits.
From a few days to two weeks, depending on how complex the process is and how many systems we need to look at. The task breakdown and a first estimate usually fit in a week.
Then that is what we tell you. It is a normal outcome of design work, and it is cheaper than six months of building something that never pays back.
Yes. The diagram and the budget are yours, and any team can implement them. That is why we write the documentation so that someone else can pick it up.
We will look at where AI pays off and where it would cost more than it returns.