Table of Contents
Background
Over more than three decades I have worked as an independent consultant in modelling, statistics and intelligent systems. In addition to my freelance work, since 2016 I have also operated through my single-person limited company, created specifically to provide a formal and contractual framework whenever required, without employees and without pursuing organisational growth — the work remains strictly senior-level and hands-on.
Academic Experience
My background includes several years working for ESADE Business School and, over the last decade, for two Australian universities, primarily in advanced quantitative modelling and structural equation modelling (SEM). This academic exposure ensures methodological rigour and a deep familiarity with how complex models are validated, interpreted and communicated.
SEM Within the AI Landscape
SEM occupies an interesting position within the AI landscape: formally it is a class of statistical models — combining linear components, latent variables and structural constraints — yet its primary value is often explanatory rather than predictive. This makes it naturally complementary to the other two AI pillars, Expert Systems and RAG-based Generative AI, because SEM produces validated hypotheses, causal structures and testable reasoning that can be incorporated into explicit rule systems or documented in a knowledge base.
Consultancy Approach
My consultancy approach intentionally avoids creating dependency: I use only open-source languages (Python, R, Prolog), lightweight back-ends and client-controlled infrastructure. Prototypes run on my sandbox servers, but final deployment always occurs on the client’s own server environment — cloud or on-premise — ensuring transparency, portability and long-term autonomy.