Ph.D. Applied Mathematics · Data & AI Leader
Alaaeddine Hammoudi
I design governed GenAI platforms for regulated industries, with a particular interest in data quality, evaluation, and the domain experts who decide whether AI can be trusted.
01Approach
Production AI fails between data, models and the people who know the business.
Most of my work sits in that gap: knowledge that can be audited, agents that can be inspected step by step, and expert feedback that becomes data instead of getting lost in spreadsheets.
02Expertise
Strategy
AI roadmaps tied to ROI and board priorities. Funding that follows demonstrated value.
Architecture
Hybrid AI Factory: ingestion, RAG, agents, model lifecycle, observability.
Governance
DORA, GDPR, AML-CFT, ISO security. Explainability and auditability by design.
Delivery
Multidisciplinary teams under SAFe. Small increments, measured in production.
03Writing
Measuring AI: what to count, and how
AI transforms each organisation differently, so its value has to be measured, not assumed. Four indicators for every use case, one guardrail to protect what makes the business different, and how to measure both honestly.
Staying in control when AI writes the code
SaaS tools, AI providers and agents now change faster than people can absorb. What control of the information system really means, why enterprises can't skip it, and three principles to hold on to when things get fuzzy.
Data maltreatment: the quiet tax on the P&L
Data quality is the symptom, data maltreatment is the process that causes it. Its mechanisms, its cost, why incentives keep it alive, and what it does to organisational knowledge and AI.