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Preparing decision systems portfolio...
Preparing decision systems portfolio...
Upload statistical outputs, regression summaries, BI reports, SAS logs, or analytical evidence. Marginalia transforms them into governed interpretations with trust scoring, weak-context detection, reasoning traces, and refusal-first AI behavior.
SAS, Python, R, Excel, CSV, Tableau exports, or plain text.
This demo currently uses structured mock artifacts to demonstrate governance behavior. Real ingestion and parsing pipelines are being integrated incrementally.
Upload a statistical output or load one of the governance scenarios to observe how Marginalia evaluates evidence quality, weak-context risk, trustworthiness, and operational readiness.
This demonstration was created as part of the Marginalia Analytics initiative to explore retrieval-augmented generation (RAG), evidence evaluation, trust scoring, weak-context detection, explainable AI, observability, and governed decision-support workflows.
Marginalia Analytics is a prototype AI decision-support platform that I designed and developed to explore retrieval-augmented generation (RAG), evidence evaluation, trust scoring, weak-context detection, explainable AI, and governed decision systems.
The public version is a demonstration environment designed to illustrate architecture, workflows, and decision logic. It showcases how analytical evidence can be transformed into governed, explainable decision support through structured evaluation, observability, and trust-aware AI workflows.
Select a mock analysis or upload an analytical output.
The system converts output into interpretable evidence objects.
Evidence adequacy, weak-context risk, and confidence are evaluated.
The system answers, qualifies, or refuses based on evidence quality.
Many analytical systems appear trustworthy because their outputs sound confident. But in practice, the underlying evidence may be incomplete, weakly related to the question, operationally unstable, or insufficient for the requested conclusion.
Marginalia is an exploration of how AI systems can become more transparent, evidence-aware, and operationally honest before organizations act on their recommendations.