DIA Digital Modernization Accelerator Frontier-Class LLM Integration and Onboarding Challenge

Enable rapid onboarding and integration of frontier-class, government-tuned Large Language Models into classified, air-gapped operational environments. DIA seeks to break out of a homogenous AI ecosystem to support comparative model evaluation, emerging capability testing, and mission-adaptive LLM selection. The solution must support Testing, Evaluation, Validation, and Verification (TEVV) protocols for mission-critical intelligence operations while mitigating algorithmic bias and reducing architectural fragility in classified intelligence environments.

Organizations with expertise in deploying and integrating advanced AI models into highly restricted, classified government environments. Applicants should have demonstrated experience with air-gapped systems, TEVV protocols for intelligence operations, and comparative AI model evaluation. Ability to work with classified data and potential TS/SCI clearance requirements expected.

1. 1Technical feasibility of rapid LLM onboarding into classified, air-gapped systems
2. 2Capability to enable comparative model analysis and output validation
3. 3Approach to algorithmic bias detection and mitigation
4. 4Architecture resilience and reduction of single-point-of-failure risks
5. 5Alignment with TEVV requirements for mission-critical intelligence operations
6. 6Integration timeline and deployment readiness

Unknown—standard DIA BAA submission process expected. Recommend checking SAM.gov for full solicitation details, page limits, white paper requirements, and proposal format specifications.