Talent Intelligence
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Model Context Protocol
AI-Native Talent Intelligence for Copilots and LLMs
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Draup integrates seamlessly with 25+ ATS, HRIS, and HCM platforms
Data Shares
Stream context-rich labor market data into your copilots, AI models, and analytics platforms.
Talent Data API
Real-time, enriched labor market data, delivered where and when you need it.
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Podcasts

How Strategic Talent Sourcing Works at Scale with Jessica Joseph & Brian Heger
How Strategic Talent Sourcing Works at Scale with Jessica Joseph & Brian Heger
July 9, 2026
Building a Skills-First Enterprise with Dr. Sandra Loughlin
Building a Skills-First Enterprise with Dr. Sandra Loughlin
April 1, 2026
Building Talent Architecture in AI Era with Leonardo
Building Talent Architecture in AI Era with Leonardo
February 27, 2026
AI-Driven Learning and Skills with Leonardo’s Skills Team
AI-Driven Learning and Skills with Leonardo’s Skills Team
February 26, 2026
How HR Can Navigate AI, Change Fatigue & Workforce Planning with Brian Heger
How HR Can Navigate AI, Change Fatigue & Workforce Planning with Brian Heger
November 18, 2025
AI’s Impact on Human-First HR with Dr. Dieter Veldsman
AI’s Impact on Human-First HR with Dr. Dieter Veldsman
October 24, 2025
Predictive skills architecture is reshaping talent intelligence with Mickey Raie
Predictive skills architecture is reshaping talent intelligence with Mickey Raie
August 6, 2025
Leveraging People Analytics for Workforce Planning with Mariya Meshcheryakova
Leveraging People Analytics for Workforce Planning with Mariya Meshcheryakova
May 6, 2025
How AI is Reshaping HR Strategy: ROI, Talent, and the Human Touch with John Pender
How AI is Reshaping HR Strategy: ROI, Talent, and the Human Touch with John Pender
April 16, 2025
Strategic Workforce Planning in the Healthcare Sector with Cody Martin
Strategic Workforce Planning in the Healthcare Sector with Cody Martin
March 11, 2025
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EAIGG

Ethical AI, Backed by Global Standards

Draup, an AI-first company, upholds global standards like SOC 2, GDPR, and ISO 27001. As an EAIGG member, we audit for bias and build trustworthy AI.

Data Trust &
Methodology
at Draup

Draup uses a structured methodology to reduce inaccuracies and prevent misleading information, with a focus on transparency and responsible data practices.

Data Transparency and Coverage

Draup sources data from diverse, vetted global inputs including public datasets, labor information, professional ecosystems, and industry research. Coverage varies by region and role, and we supplement limited areas with alternative sources and analyst-led modeling.

Profiles Behind the Numbers

All aggregate insights related to buyers, roles, skills, and locations are grounded in structured, anonymized human and organizational profiles rather than pure extrapolation. This ensures context, reliability, and protection of personal identities.

Data Hygiene and Integrity

We maintain a clean, accurate data environment by removing duplicates, outdated entities, and invalid records. Automated checks powered by ML models and analyst reviews work together to ensure clarity and accuracy for every entity, such as buyer, role, skill, technology, or company.

AI Governance and Bias Mitigation

Our governance framework combines automated evaluations with Human-in-the-Loop reviews. We use statistical checks, audits, and cross-source comparisons to reduce demographic and structural bias before insights reach the platform.

Compensation Guardrails

Compensation insights use blended, directional inputs such as aggregated ranges, benchmarks, modeled distributions, and market signals. We distinguish clearly between modeled and reported data and apply guardrails to prevent over-interpretation.

Hourly and Frontline Workforce Coverage

For hourly, frontline, and blue-collar roles where digital visibility is limited, we incorporate government datasets, localized labor information, and specialized partners to build a more complete and balanced view of workforce supply and demand.

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