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Ed Company Profile

An Overview of the U.S. Department of Education: History and Purpose · Department of Education logo. ED.gov Feedback Form ...

Industry: GovTech

Segment: Government Agency

Team size: 1001-5000

Competitors: Myth, Fact, Fafsa.ed.gov

Generated by Kustiq, an AI company intelligence platform for B2B teams. Multi-stage pipeline: AI classification + live web scraping + SMTP-verified contacts + 12-factor rule-based churn engine.

Company profile

Ed

An Overview of the U.S. Department of Education: History and Purpose · Department of Education logo. ED.gov Feedback Form ...
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Ed logo

AI-verified profile

An Overview of the U.S. Department of Education: History and Purpose · Department of Education logo. ED.gov Feedback Form ...

Employees

1001-5000

AI Classification

GovTechGovernment Agency

The U.S. Department of Education is a federal cabinet-level agency responsible for promoting educational excellence, equity, and opportunity across the United States. It administers federal financial aid, discretionary and formula grants, and policy programs serving students, K-12 schools, districts, and higher education institutions nationwide.

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Social Links

Linkedin

Competitors

MythFact

Ed is profiled by Kustiq, an AI company intelligence platform for B2B teams. Ed is one of 30 government agency companies in our GovTech directory. Similar profiles include Figma, Business Process Optimization for the AI Era, Connect, protect, and build everywhere, and AI-Powered Online Recruitment Software for Agencies.

How Kustiq built this profile

Hybrid pipeline: deterministic verification + Claude reasoning

  • Live web rendering via Browserless headless Chrome, not a stale cached scrape.
  • Search intelligence via Serper API across {company site, LinkedIn, press, GitHub}.
  • Claude Sonnet 4.6 classifies vertical, segment, and growth signals; never used for facts.
  • 12-factor churn engine produces a deterministic risk score with zero LLM calls at scoring time.
  • SMTP RCPT TO handshake verifies every contact at delivery time on full profiles (paid plan).

Read the full methodology in our LLM-readable reference or compare against the deterministic-vs-AI framework.

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