AI - Accountable Intelligence

Your board wants an AI strategy but you are not sure where to start, what is realistic, or how to avoid expensive mistakes. We bring a PhD in AI and Machine Learning combined with decades of practical technology leadership and real examples you can see to help you move from interest to implementation.

We don't like the term Artifical Intelligence. It's broad and covers a lot of things but most newly-minted experts just think of using Large Language Models everywhere, which can run away with cost and could well introduce errors and hallucinations. Magilium consults on AI implementation, though only the sort we are prepared to call Accountable Intelligence.

Our approach is more surgical: AI goes in where it does something that genuinely could not be done without it, or where it materially improves efficiency or reach, and it stays out everywhere else. That is, it goes only where the result is cost-effective.

We have no interest in scattering models across a business so that someone can report that AI is present, since that habit burns through tokens, inflates the monthly bill and leaves a company paying for capability it never actually uses.

Accountability starts before a single model is chosen. We work out which model fits the task rather than reaching by default for the newest frontier engine and the costs that come with it; we check whether a conventional, non-AI automation would do the same job more cheaply before recommending AI at all; and we set every deployment inside an overall security and risk strategy, so that adding intelligence to a process does not quietly open a new way into the business. That discipline, deciding what to use, what to leave out and what to guard against, is why we call it Accountable.