Decide what to build before you build it.
Most expensive technology mistakes are made in the first three weeks, on a whiteboard, by people who have not yet had to operate the result. We have — which is the only reason our advice is worth anything.
“Do not build this” is a legitimate outcome of an engagement, and we have delivered it. It is cheaper for you than the alternative and better for us than a project nobody wanted.
Advice you can hand to an engineering team.
Every engagement ends in artefacts, not a slide deck and a handshake. If your team cannot start building from what we leave behind, we have not finished.
A recommendation, with the reasoning attached
What to build, what to buy, what to leave alone — and the tradeoffs behind each, written down so your team can argue with it after we leave.
A reference architecture
Services, data model, integration points, where the AI sits and where it does not. Concrete enough that an engineering team can start against it on Monday.
A costed roadmap
Sequenced phases with what each one costs to build and to run. Cloud and model costs projected at your actual volumes, not at demo volumes.
An AI feasibility read
Whether a model genuinely beats conventional code for your problem, what accuracy is achievable on your data, and what it takes to evaluate it honestly.
Four situations we recognise immediately.
You have an AI mandate and no obvious place to spend it
We find the two or three processes where a model actually creates leverage, and say which of the rest are better served by ordinary software.
You are choosing between building and buying
We cost both honestly, including the operating burden of the thing you build — which is the number that is usually missing.
Your platform is holding back the growth target
We find the specific constraint — data model, tenancy, a synchronous path that should not be — and sequence the work to remove it without a rewrite.
The pilot worked and production is not going well
Demos and production fail differently. We identify what the demo did not have to survive: load, bad input, edge cases, cost at volume, and the people operating it.
Not sure what you need yet?
That is the normal state, and it is a good reason to talk rather than a reason to wait. Describe the problem in your own words — we will tell you whether it is a strategy question, an engineering question, or something you already own the answer to.
Or write to contact@astalabista.com. A person reads it, and replies within one working day.