The Dubstrata Engine ingests, processes, and structures alternative data from a wide array of public, prediction, and financial sources to build a point-in-time causal knowledge graph.
Direct integrations with decentralized prediction markets to capture crowdsourced probabilities and sentiment spikes.
Global spot pricing feeds mapped to digital assets and macro indices to compute correlation metrics.
Information sources leveraged by the self-reflective JIT engine to populate background facts and entity definitions.
Official gazettes and news feeds scraped to verify regulatory statements and track trends.
While these public feeds populate the default global schema, all tenant-specific documents ingested via the ingest_knowledge tool (with is_private: true) are isolated into dedicated, encrypted namespaces. These private sources are completely excluded from other tenants' queries.
