AI-powered skills and job architecture intelligence for enterprise HR leaders, transforming Oracle from a system of record into a living, market-aligned talent intelligence platform.
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Without disrupting your existing Oracle workflows, Draup continuously enriches your environment with real-world market intelligence; turning static HR data into dynamic workforce strategy.
HR teams in Oracle environments face persistent data quality challenges. Draup closes the gap between internal records and the market reality.
Draup follows a structured pull-process-push model that keeps your Oracle environment clean and current, operating invisibly alongside your existing HR workflows.
Draup follows a 9-step deployment model built to protect data integrity and move at your pace, from discovery through live production sync.
Across the talent lifecycle, Draup intelligence activates within Oracle's native workflows to improve every HR decision.
Tangible improvements across your Oracle HCM environment from day one of production deployment.
Enterprise-grade architecture built on Oracle’s native APIs with full compliance alignment.
The integration enables synchronization of job profiles, skills, competencies, and job architecture data between Draup and Oracle HCM; enriching your Oracle HR data with Draup's global talent intelligence insights without disrupting existing workflows.
Yes. During the solution design phase, Draup Professional Services reviews specific use cases, designs required workflows, and identifies the relevant objects and fields that need to be synchronized. Custom field mappings are fully supported.
Job profiles are identified using a unique job code or job profile identifier available within the Oracle HCM instance. This ensures precise targeting without unintended overwrites across the profile catalog.
Sync frequency is fully configurable based on business needs: real-time event-driven sync, scheduled intervals (daily, weekly), or on-demand batch updates. The configuration is agreed upon during the requirements discussion.
Conflict resolution rules, such as source of truth priority or latest-timestamp-wins logic, are defined during the mapping and requirements discussion phase. All conflict resolutions are logged for audit purposes.
A full dry-run in a sandbox environment validates mapping, syncing, and processing before any production deployment. The validation generates a detailed report for stakeholder sign-off, ensuring zero surprises at go-live.
Errors are logged, categorized, and reported. Administrator alerts can be configured for immediate notification. Detailed diagnostic logs support rapid troubleshooting, and no partial writes are committed without resolution.
Yes. All synchronized data changes are tracked in immutable audit logs, providing full transparency, traceability, and compliance evidence for internal reviews or regulatory requirements. Role-based access control limits who can view or export audit records.
Access insights via API, custom data feeds, the Draup platform or using MCP
Best for
Embedding live insights in workflows without storing data
Best for
Analytics at scale & joining Draup with internal data
Best for
Fastest time-to-value,
no build required
Best for
Real-time Al workflows & LLM applications
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. Learn more
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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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.
By filling up this form, you agree to allow Draup to share this data with our affiliates, subsidiaries and third parties
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