Service description

This is where the hard problem meets the ideal solution. AmPhi Labs’ AI/ML engineers and Applied AI experts turn your chosen concept into a working prototype or MVP — a Minimum Viable Product — running on your real data. It sits alongside your systems rather than inside them, so testing risks nothing live, and it is built so that wiring it in later stays efficient.

What we build

A few examples of what teams ask us for. If your problem needs a different shape, we build that instead.

ML for prediction — demand, yield, quality, energy use, equipment failure.
Automation workflows — the repetitive handoffs between your systems and inboxes.
AI agents — workflows that act: triaging requests, reading documents, checking specs.
Optimisation & recommendation engines — pricing, routing, scheduling, material substitution.
Tailored apps — optimising a production run, automating a reporting cycle, helping R&D narrow a materials shortlist in days instead of months.
Customer clustering — the segments in your own data, not the ones you assumed you had.
Online ML pipelines — a live scoring service behind an app, dashboard or API.

The concept usually comes from the solution selected in your AI Value Scan, or a winning idea out of the Hack Pack™ or AI-Powered Idea Lab. Your own concept works just as well.

What’s included

Scoping: the concept, the data, and the success metrics that decide whether it works.
A working prototype or MVP on your real data, built by our AI/ML engineers and Applied AI experts.
Usage and success metrics built in.
The test set-up, and the read-out once real users have used it.
A profit-and-planet scorecard for the concept.
An honest scale / fix / drop verdict.

Testing support, production deployment and continued development are quoted separately.

How it works

1 · Scope

We lock the concept, the data, and what “it works” has to mean for real users.

2 · Build

Our AI/ML engineers and Applied AI experts build it on your real data, at unmet speed.

3 · Test & verdict

Real users try it, the metrics record what happens, and your team gets the honest call: scale, fix or drop.

Who tests it

Real users, not your project team. The people who will live with it are the only ones whose reaction tells you anything.

An honest note on scale

If the verdict is scale, we tell you straight whether we are the right team to take it into production and scale it. Where it fits us, we deploy and keep building in agile sprints. Where it does not, we point you to partners we would trust with it ourselves.

Who it’s for

Teams who have selected a high-potential AI solution concept and want to watch real users use it before they commit a scale-up budget.

Rapid AI Prototyping