National Guard Bureau (NGB) Enterprise Data and Artificial (AI) Program
Overview
Buyer
Place of Performance
NAICS
PSC
Set Aside
Original Source
Timeline
Qualification Details
Fit reasons
- NAICS alignment with historical contract wins in similar service areas.
- Scope strongly matches core technical capabilities and delivery model.
Risks
- Past performance thresholds may require one additional teaming partner.
- Potential clarification needed on staffing minimums before bid/no-bid.
Next steps
Validate eligibility requirements, assign capture owner, and schedule partner outreach to confirm teaming strategy before submission planning.
Quick Summary
The National Guard Bureau (NGB) has issued a Request for Information (RFI) for market research to inform the development of an Enterprise Data and Artificial Intelligence (AI) Program. This RFI seeks to identify industry capabilities, innovative approaches, and potential solution architectures to establish an enterprise-wide data environment and AI-enabled decision advantage for NGB. Responses are due May 22, 2026.
Purpose & Scope
This RFI aims to gather information on available technologies and strategies for creating a federated and interoperable data environment across NGB, integrating disparate data sources, and implementing Zero Trust principles. The program's goal is to improve situational awareness, enhance decision-making, reduce redundancy, and enable advanced analytics and AI capabilities for domestic operations, federal missions, and CJADC2.
Key Requirements & Capabilities Sought
- Data Ingestion & Integration: Support for structured, semi-structured, and unstructured data from various sources, including batch and real-time ingestion, API integration, data streaming, and secure data exchange. Must integrate with existing DoD and service data platforms and enable federation.
- Data Storage & Management: Scalable, multi-tiered data architecture (e.g., data lake, data warehouse, data fabric/mesh) with capabilities for data normalization, transformation, tagging, standardization, metadata management, data cataloging, and discoverability.
- Analytics & AI Enablement: Advanced analytics, self-service analytics, ad hoc querying, data visualization, and enablement of AI/machine learning model development, deployment, and governance for predictive analytics, automation, and decision support. Low-code/no-code tools are desired.
- Governance & Data Management: Comprehensive data governance framework (ownership, stewardship, quality, lifecycle management) and tools for data lineage tracking, validation, monitoring, and remediation.
- Security & Zero Trust Architecture: Design must implement Zero Trust principles with continuous verification, robust identity and access management, role-based/attribute-based access controls, least privilege, micro-segmentation, data-level security, continuous monitoring, threat detection, and automated response.
Submission Details
NGB seeks to understand available technologies, integration approaches, Zero Trust best practices, AI/analytics strategies, recommended implementation approaches, and potential risks/challenges. Responses and questions should be submitted via email to Jeannie Botteri (jeannie.m.botteri.civ@army.mil) and LaKia Johnson (lakia.l.johnson.ctr@army.mil).
Contract & Timeline
- Type: Request for Information (RFI) / Sources Sought (Market Research)
- Set-Aside: Not applicable for this RFI.
- Response Due: May 22, 2026, 8:00 PM ET
- Published: April 24, 2026
Additional Notes
This RFI is not a solicitation and does not obligate the Government to a future procurement. The information gathered will shape requirements and refine acquisition strategy. The solution is expected to operate within or integrate with a FedRAMP High-authorized cloud service provider and align with DoD-approved cloud environments, supporting unclassified and classified data (Impact Levels 2-6, Public up to SECRET). The suggested NAICS code is 518210 (Data Processing, Hosting, and Related Services) and PSCs include D399, D302, D308, D307, D310; bidders are encouraged to suggest alternatives.