Most AI programmes fail on the choice of problem, not the model. We audit what you actually do, what it costs and where the time goes, then cost the options honestly and tell you which are worth building, including the ones that are not. Every candidate comes back with a cost, a latency budget and an achievable accuracy, estimated before anything is built, so the trade-offs are visible while they are still cheap to change. A meaningful share of these end with a recommendation not to build, which is the answer that saves the most money.
The same four moves on every engagement of this kind. No discovery phase that bills for months before anything runs.
We sit with the people doing the work and map what actually happens, including the parts nobody has written down. That is where the automatable work hides.
Each candidate gets a cost, a latency budget and an achievable accuracy, estimated before anything is built, so the trade-offs are visible while they are still cheap to change.
You get a shortlist ordered by expected value, and an explicit list of the ideas we think you should not build. The second list is usually the more useful one.
A sequenced plan, a risk register and a cost model, written so your own engineers can execute it. You are not obliged to use us to do it.
We look at what you do, what it costs, and where the time goes, before anyone says the word model. Most of the value in this work is finding the two processes worth automating and naming the eight that are not.
Every candidate gets a cost, a latency budget and an achievable accuracy, estimated before anything is built, so the trade-offs are visible while they are still cheap to change. A list without numbers is a wish list.
Some of what arrives framed as an AI problem is a data quality problem or a process problem wearing a costume. We would rather tell you that in week one than take the engagement and hand you a model that cannot work.
Sequenced by risk rather than by ease, with the first two weeks specified in enough detail that a team could pick it up without us. The plan is a deliverable, not a pitch for the next phase.
Not sure which AI ideas are worth building?
Scope this with usThis practice exists because the expensive mistake in AI is not a technical one. It is spending two quarters on the wrong candidate. The work is deciding what to build, in what order, and what to leave alone.
Some tasks want a model. Plenty want a rule, a report or a better form, and cost a fraction. We separate the two before anyone commits budget.
A number you can hold us to, with the assumptions behind it written down, so the trade-off is visible while it is still cheap to change.
Token cost, latency and the infrastructure around it, modelled at the volume you actually expect rather than at demo scale.
A meaningful share of these audits end with a recommendation not to build. That is the answer that saves the most money, and it is why the rest of the advice is worth anything.
Every cost and accuracy number has the assumption behind it written down, so you can challenge the assumption rather than take the number on faith.
The recommendations come from the same team that would build it, so nothing lands on your desk that has never survived contact with production.
You know what the audit costs and what you get before it starts. No discovery that expands until it becomes the project.
The questions that come up most on a first call about this practice, answered the way we would answer them on the phone.
Two weeks for the standard version, fixed price. Longer only when there are several business units to cover, and we would say that before you commit rather than halfway through.