🏗️ The Problem: The "Black Box" of AI Summaries
In the early development of VFS, the technical challenge wasn't just extracting information—it was proving where that information came from. Traditional AI summarization often creates "hallucinations"—plausible-sounding statements that lack a direct evidence trail. For a mission built on trust, this was unacceptable.
🚀 The Evolution: Source-First Architecture
Era I focused on Provenance. We re-engineered the ingestion and neural pipeline to ensure that no claim could exist without an anchor.
- Article Clustering: We moved from single-article analysis to multi-source event clustering.
- Source Auditing: We implemented the first
source_auditschema, allowing administrators (The Engineers) to see exactly which articles constituted a news event. - The First Triple: This era saw the birth of our atomic claim structure:
Subject -> Predicate -> Object, each with a mandatorysource_url.
⚖️ Strategic Impact
By the end of Era I, VFS had achieved 100% agreement between the data clusters in our lake and the evidence presented in our UI. We had successfully replaced the "Black Box" with the foundation of the glass box.