Evidence Factory: Automated Research Synthesis for Enterprise AI

Marco van Hurne (Eigenvector Research)

Abstract

We present the Evidence Factory, a systematic approach to building and maintaining the research evidence base for enterprise AI decisions. The Evidence Factory automates research collection, synthesis, and relevance assessment.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper

Eigenvector Research — Marco van Hurne

How this paper contributes to solving the Zone III problem (PASF-PADE)

The Evidence Factory is the meta-framework that makes Eigenvector Radar possible. Systematic evidence collection and synthesis is the foundation for evidence-based enterprise AI decision-making.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

→Evidence Factory methodology
→Automated research synthesis
→Enterprise AI evidence base

Topics

research synthesisevidence baseautomated researchknowledge management

Relevance Scores

Long-Horizon Score82

Enterprise Score94

Completeness86

Paper Info

Year2025

VenueEigenvector Research

Typeindustry report

ChapterCh. 7

Authors1

Zone III Analysis

Frameworks

PASF PADE