Case studies

Case study 1

The situation. The client had already spent eleven months building on a low-code platform. The team was experienced, the vendor was reputable, and the approach still hadn't produced a working result they could put in front of their own customers.

The stakes. Starting over is normally the outcome everyone tries to avoid — more months, more budget, no guarantee the second attempt goes better than the first. At eleven months with nothing to show, the client was weighing whether to keep pushing an approach that wasn't working or absorb the cost of a restart.

The decision. In May 2026, the client chose to rebuild from zero — this time on Altamira's AI SDLC, against the same requirements the low-code attempt had been working from.

The result. The system reached production in three months. Not a demo, not a pilot — a live system the client uses today, with change requests and defect fixes moving through the same pipeline that built it, the same way any delivered software gets maintained.

What this means for you: if a previous attempt at a build has stalled, restarting doesn't have to mean starting the clock over at zero risk tolerance. This is what Foundation's AI-Accelerated Software Delivery looks like end to end.

Case study 2

The situation. The client needed a new enterprise system built from scratch, without the budget or year-long runway a conventional build usually requires.

The stakes. A greenfield build is normally where team size becomes the ceiling — more requirements mean more developer-weeks, and headcount takes months to hire and ramp. The client needed to know whether moving faster meant compromising on scope, or whether there was a way to keep both.

How it ran. Work started in February 2026. By the end of April, the first three epics were approved and already built on the AI SDLC. From there, each epic moved into build as soon as it was approved — approval, not delivery capacity, became the actual bottleneck.

The team. 1.2 people, part-time: half a Requirements Manager, half a QA engineer, a tenth of a solution architect, a tenth of a project manager. No developers on the roster. For comparison, Altamira's own estimate of a conventional team for the same scope is five people full-time — that comparison is Altamira's estimate, not a measured third-party benchmark.

The result. The system shipped in six months and has been in production since, with the client raising ordinary change requests through the same pipeline that built it.

What this means for you: if team size has been the reason a build gets scoped down or pushed out, this is what it looks like when specification — not headcount — is the constraint instead.