AI Product Engineering
Taking an AI product from an idea to something people pay for. Which includes the uncomfortable early part where the honest answer might be that a model is the wrong tool for this, and finding that out in weeks rather than after a year of building.
We take the delivery. You keep the bet.
AI product engineering means we own how it gets built and whether it actually works on your cases. What stays with you is the commercial judgement about whether it is worth building at all.
Ship something small before you believe anything.
AI product work goes wrong in a specific way: the demo is extraordinary and the product never arrives. A model that is right four times out of five looks like magic in a meeting and is unusable on a desk. We would rather put a narrow version in front of real users early and find the fifth case, because the correction is the valuable part.
The assumption is written before the code
One sentence, falsifiable, agreed. If nobody can write it, the project is not ready and we will say so.
An eval set before a model is chosen
A few dozen of your real cases, with the answer a good employee would give, agreed before anyone picks a model. Without it every release is an argument about vibes and the model you end up with is the one that demoed best.
The first release is embarrassingly small
Narrow enough to ship in weeks, to one team who will tell you the truth. It teaches you more than a quarter of planning and costs a fraction.
Killing it counts as success
If the measurement says no, stopping is the right answer and we will recommend it. A partner who never recommends stopping is selling hours.
Three ways to work with us. This is one.
Same bench, different shape. Pick the wrong one and you pay for coordination you did not need.
Most AI product failures are not model failures.
Good fit
- You have an idea and no way to tell whether a model is good enough for it.
- A pilot demoed beautifully and has been stuck for months.
- You need engineering judgement about where AI fits, not just capacity.
- The scope will change, and everyone already knows it.
Poor fit
- The specification is settled and you want it executed. Use augmentation.
- You want the accuracy guaranteed before the first eval exists.
- You want an agency to own the commercial risk. We will not.
Before you book the call.
We will read it, and then ask what it is for. Often the spec is right and we build it. Sometimes it describes a solution to a problem that has moved, and the kinder thing is to say so in week one.
Scope. Integration adds a model to a product that already exists and already has users. This is for the product that does not exist yet, where the question of whether anyone wants it is still open and the model is one of the things being tested.
Then we find that out in week three on your own cases, which is the entire point of building the eval set first. Sometimes the fix is retrieval, or a narrower scope, or a human in the loop on the hard cases. Sometimes the honest answer is that this one should be ordinary software, and we will say so.
Then we tell you, with the measurement that says so, and you stop. We would rather lose the next phase than bill for a year of building something the evidence already rejected.
Bring the assumption, not the feature list.
Thirty minutes on what you believe and how you would know if you were wrong. That call alone is usually worth having.