AMSAFIS – Data, Models and Reasoning for High-Stakes Decisions on AMSAFIS

Agent

Mon, 01 Jan 0001 00:00:00 +0000

Composition of AI Capabilities

An Agent is not a separate pillar. It is a combination layer that integrates existing AI components into a single system.

At amsafis, Agents typically combine:

Expert Systems + LLM, or
Machine Learning + LLM

The role of the Agent is to coordinate these components, not to replace them. Each part keeps its function:

Expert Systems provide structured, rule-based reasoning
Machine Learning models provide predictive outputs
LLMs provide interpretation and generation capabilities

The result is a system that can process inputs, consult multiple sources, and return a coherent and structured response.

Contact

Mon, 01 Jan 0001 00:00:00 +0000

If you would like to discuss a project, data challenge or collaboration, you can contact me directly.

Email
📧 xavier.suriol@amsafis.com

amsafis
Data, Models and Reasoning for High-Stakes Decisions

Expert Systems

Mon, 01 Jan 0001 00:00:00 +0000

What an Expert System Is and Why It Matters Today

Expert systems are AI tools based on user-defined rules and structured knowledge, designed to solve problems in a formal, symbolic, and fully consistent way.

Unlike generative models, an expert system always returns the same answer for the same inputs, and you retain full control over what knowledge is used and how decisions are made.

An expert system can integrate:

Rules created by domain experts

Health

Mon, 01 Jan 0001 00:00:00 +0000

Healthcare Solutions — Data, Knowledge and Intelligent Decision Support

Healthcare generates complex, heterogeneous and high-stakes data.

At amsafis, we integrate Machine Learning, Expert Systems and RAG-LLM technologies to transform clinical information into reliable predictions, interpretable reasoning and actionable decision support.

Our objective is simple:

to help clinicians and health organisations make better decisions with less uncertainty.

1. Epistasis and non-linear models in Health

(no GWAS, no additivism)

The biological and clinical world is not linear.

Machine Learning

Mon, 01 Jan 0001 00:00:00 +0000

Machine Learning — Predictive Modelling and Data-Driven Insight

Introduction and Value of Machine Learning

Machine Learning (ML) is the technology that allows data to generate useful and actionable knowledge. Unlike systems based on fixed rules, ML learns patterns directly from real data and builds models capable of predicting, classifying or recommending automatically.

It is an iterative process that evolves over time and adapts to changing environments.

RAG-LLM

Mon, 01 Jan 0001 00:00:00 +0000

AI-powered Knowledge Solutions

Solutions Based on Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) make it possible to transform enterprise information into accessible and actionable knowledge.

The combination of document databases with generative AI capabilities delivers precise, relevant answers that are aligned with each organisation’s internal documentation.

Typical applications include:

Commercial chatbots that answer customer queries using company knowledge.
Internal assistants that streamline workflows and support employees.
Smart document manuals offering direct, context-aware answers.
Compliance and policy guidance systems ensuring consistent decisions.
Knowledge hubs that unify dispersed information for quick retrieval.

👉 Try our RAG Sandbox using ChatGPT-3.5