Financial markets move on news, but turning raw articles into actionable intelligence requires human-scale effort at machine scale. Reading 290,000 articles daily to extract sentiment per asset, resolve entities to tradeable tickers, identify economic claims, and trace contagion chains across sectors is impossible for a central team to do with consistent quality. A decentralized network of miners can do the work in parallel, but verifying that work at scale without paying for it twice is the hard problem.
SN45 solves this through a lease-and-audit model. Miners receive batches of articles, tweets, and Telegram messages and run a multi-stage NLP pipeline on each one—extracting sentiment, entities, economic data, event fingerprints, and narrative keywords. Validators hand out work in batches and spot-check a sample from each by re-running the same pipeline themselves, without seeing what the miner produced. If the sample reproduces exactly across all four validation tiers, the entire batch is accepted and rewarded. If it fails any tier, the whole batch is discarded and earns nothing. This keeps volume honest without doubling the work.
The subnet analyzes roughly 290,000 articles per day from over 12,000 sources. Miners are paid per verified item, scaled by a reputation multiplier that reflects their audit history. Validators run the same hardware as miners, broadcast signed epoch snapshots to peers, and set on-chain weights from the aggregate. Verified output feeds a separate cross-article layer that clusters real-world events and matches them to long-running market narratives, which surfaces in charts, screeners, and APIs downstream.
