Holmes Noise Filter is a high-signal data filter that automatically extracts underlying data, historical context, and primary sources from over 10,000 sources, bypassing AI-generated spam and SEO clutter to deliver verified intelligence for researchers and professionals.
What is Holmes Noise Filter?
Holmes Noise Filter is a web-based research tool developed by Hermes Data. It ingests raw data streams from over 10,000 sources—including policy releases, corporate filings, and global market narratives—and outputs structured intelligence briefs, ranked alerts, and actionable scenario paths. It automatically excludes AI-generated summaries, sponsored content, and algorithmic bias, delivering only verified sources, academic papers, and raw facts.
Key Features
- Deal Flow — Automated opportunity detection that converts unstructured signals into structured deal memos, highlighting potential investments and dislocations.
- Deep Research — Autonomous research agents that recursively investigate every lead, mapping causality and isolating opportunities with measurable impact.
- Risk Shield — Real-time threat vectors that identify regulatory shifts and supply chain breaks before they hit public headlines.
- Zero-Fluff Extraction — Automatically filters out AI-generated spam, superficial blog posts, and low-value content, saving hours of manual filtering.
- Streamlined Workflow — Condenses hours of cross-referencing into seconds of clear, actionable intelligence, directly from the dashboard.
Who is it for?
- Financial analysts — Detect unseen market dislocations and monitor regulatory shifts using real-time news filtering and deal flow signals. Used by the internet's largest companies for internal research.
- Policy researchers — Track policy releases and filings across global sources to map causality and anticipate changes.
