| Best-fit delivery model | An enterprise people-intelligence foundation for assistive and agentic workflows across sourcing, screening, executive search, hiring, mobility, and workforce decisions | Recruiter-guided sourcing and ATS rediscovery, Sam inbound evaluation and screening, plus Spot’s AI-and-human candidate-delivery model |
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| Core data model | 3D contextual data on 800M+ people across 100K+ sources, with 300,000+ searchable attributes | 1B+ profiles across public sources and ATS data, with strong technical and specialized talent signals |
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| Sourcing coverage | External, ATS rediscovery, internal, referrals, alumni, and CRM on one contextual foundation | External search and ATS rediscovery in Recruit; internal mobility is a separate Grow product story |
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| Technical signals | Patents, publications, skills, career trajectory, company context, and relationship signals | Deep GitHub, patent, publication, cleared, healthcare, and other specialized profile filters |
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| Candidate evaluation | Evidence-based attributes connect qualifications with trajectory, context, and networks | Workspaces supports criteria and scorecards; Sam evaluates applicants against role requirements with explanations and audit trails |
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| ATS and inbound workflows | Rediscovery, inbound review, warm-channel activation, outreach, and assessment share one context layer | Recruit supports ATS rediscovery and outreach; Sam adds continuous inbound evaluation, ranked results, explanations, and optional AI screening |
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| Internal mobility | Included in the broader platform scope alongside external and warm-channel sourcing | Available through SeekOut Grow rather than the standard Recruit workflow |
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| Managed service | Platform-led model designed to build reusable internal workflows and talent knowledge | SeekOut Spot combines AI and expert recruiters to deliver interview-ready candidates in about two weeks |
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| Talent insights | Market movement, company context, sourcing analytics, networks, and workforce intelligence | Labor-market analytics, compensation benchmarks, talent-pool data, and pipeline analytics |
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| AI assistant model | Assistive, agentic, and build-your-own AI grounded in shared 3D talent context | Workspaces, SeekOut MCP, Sam inbound evaluation and screening, and Spot support recruiter-led workflows |
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| Implementation, governance, and value measurement | Define the first use case, required channels and context, user control points, and a baseline for recruiter effort, slate acceptance, warm-path contribution, adoption, and cross-workflow reuse | Confirm the chosen Recruit, Sam, Grow, or Spot scope; the human and AI responsibilities; data and ATS requirements; explanations and audit needs; and the operational outcomes used to judge success |
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