An honest comparison
A demo and a production system are not the same build.
A large number of firms will build you an AI prototype quickly and cheaply. The difference shows up at the point where it has to run reliably against real data, and stay working after launch.
Web and app development shops with an AI practice attached, typically strong at interface work and quick to produce something demonstrable. Depth in the parts that come after launch - evaluation, drift, retraining, inference cost - varies enormously between firms.
Built by engineers whose background is production ML systems. Evaluation, guardrails, monitoring and handover are part of the deliverable rather than a later phase.
The gap is rarely visible in the demo. It appears in month three, when the data drifts, the costs climb, and nobody can say whether the system is still working.
8 weeks
Typical engagement, first scoping call to handover.
One senior pod, scoped to a single metric and priced against it, ending in a running system with its evaluation harness and the documentation your team needs to own it.
If you need something demonstrable for a pitch or a validation test, this is often the sensible purchase and we would say so.
Many are genuinely excellent at the surface layer, which is a real discipline and not one to dismiss.
Not everything needs production ML rigour. A thin wrapper over a model API sometimes really is the whole job.
What you give up
Evaluation, drift, retraining and inference cost are where AI systems actually fail, and they arrive after the launch.
Ask directly how they measure whether a model is still working, and what happens when it stops.
A prototype built without production concerns often has to be rebuilt to survive real load and real data.
Our engineers have run models in production, where the interesting problems are the ones that appear after launch.
You get the harness that proves the system still works, not just the feature that demos well.
Documented for your team to own and extend.
What you give up
If you genuinely only need something to demo next month, we are the wrong choice and a prototype shop is the right one.
Some AI features really are a thin wrapper over an API. Paying for production ML rigour there is waste.
We ship the interface that carries the system, but brand and product design craft is not what we compete on.
A direct conversation about the problem, the metric, and whether we are the right shape for it. If another option on this page fits you better, we will say so.
