amsafis is a specialised consultancy focused on transforming complex data into reliable predictions, interpretable logic and actionable knowledge.
The company is small by design: the work is carried out directly by a senior expert with more than three decades of professional experience in modelling, statistics, operational analytics and intelligent systems.

This ensures rigor, personal responsibility and solutions that can be understood, validated and trusted.

Three complementary pillars

Modern decision support requires more than one technique.
At amsafis, all projects combine one or more of these pillars, depending on the problem.

1. Machine Learning — Predictive Modelling and Pattern Discovery

Machine Learning provides the mathematical backbone for prediction, risk scoring, anomaly detection and non-linear pattern discovery.
It is particularly effective when real-world data include noise, interaction, heterogeneity or limited sample sizes.

Examples of techniques:

• non-linear and additive models
• multilevel and hierarchical modelling
• functional data analysis
• association rules
• differential-equation-based models
• neural-network optimisation for non-linear structures

ML is used both for direct prediction and as a source of quantitative evidence that later feeds Expert Systems or rule-based reasoning.

2. Expert Systems — Transparent Rules and Explainable Logic

Expert Systems convert clinical, industrial or organisational knowledge into explicit rules that can be audited, traced and verified.
This approach provides clarity and full explainability, which is essential in regulated environments such as healthcare, manufacturing quality or operational safety.

These systems support:

• diagnostic or decision pathways
• alert rules and safety protocols
• quality and compliance logic
• hybrid reasoning combining rules + data-driven prediction

A rule-based sandbox for rare-disease diagnosis is available in the website as an interactive demonstration.

3. RAG-LLM — Retrieval-Augmented Understanding of Documents

Large Language Models are only reliable when connected to verified sources.
RAG pipelines link LLMs with scientific articles, guidelines, technical documents or internal reports, ensuring:

• evidence-based answers
• citation and traceability
• structured extraction
• safe interaction with unstructured text

This makes it possible to query hundreds of pages of technical or clinical documentation in natural language, with transparent references and controlled behaviour.