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# Companies Every company mentioned in a vacancy gets an **automatically enriched profile** — verified, structured, and continuously improved. --- ## Company Profiles Each profile includes: | Field | Source | |-------|--------| | **Name** | Normalized from all variant mentions | | **Logo** | Auto-fetched from website favicon | | **Website** | AI-resolved corporate domain | | **Industry** | LLM-classified from context | | **Size** | Estimated: Startup to Enterprise | | **Location** | Headquarters if detectable | | **Open Positions** | Count + list of active vacancies | | **Domains** | DeFi, NFT, Gaming, etc. | --- ## How Enrichment Works When a new company name appears in a vacancy signal: 1. **Normalization** — "Binance", "binance.com", "Binance Labs" become a single entity 2. **Deduplication** — Fuzzy matching prevents duplicate profiles 3. **Website Resolution** — AI identifies the most likely corporate domain 4. **Logo Retrieval** — Fetched from the resolved website 5. **Metadata Extraction** — LLM extracts industry, size, HQ from available data 6. **Continuous Learning** — Each new vacancy referencing the company adds more signal The more vacancies reference a company, the richer its profile becomes. --- ## Using Company Data ### For Competitor Analysis Search for companies in your space. Compare: - How many positions they have open (hiring velocity) - What roles they are hiring for (strategic direction) - What salaries they are offering (compensation benchmarking) ### For Market Mapping Use company data to understand market structure: - Which companies dominate which domains - How company size correlates with hiring volume - Which industries are seeing the most activity ### In Studio Pipelines Use the **Companies** source node in Analytics Studio to build company-centric analyses — top hirers, industry breakdowns, size distributions.
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