Data and News Methodology
How The Neural Ledger collects, normalizes, analyzes, publishes, revises, and exposes source-linked news intelligence.
Collection
The service receives structured upstream news-story records and their available source references. It records publication and update times and does not represent automated ingestion as first-hand reporting.
Normalization
TNL Bot converts incoming records into consistent titles, summaries, categories, countries, entities, claims, sources, and evidence counts while retaining the upstream story identifier.
Analysis
Automated models may classify stories and estimate possible market impact, affected assets, sectors, urgency, and verification signals. These fields are analytical context rather than confirmed outcomes or personalized financial advice.
Editorial synthesis
For long-form work, the service expands one primary brief per post, researches additional sources through Docdex and public-web discovery, retains citations, and validates content-type word ranges before publication.
Publication and revision
Briefs expose their source evidence and updated time. When material stored fields change, TNL records a bounded revision entry describing which fields changed without inventing a correction reason.
Access and reuse
Public articles, sitemaps, RSS, Atom, and the same-origin API documentation provide stable machine-readable discovery. Subscription and API plans fund the service but do not purchase editorial outcomes.