Mission-Specialized Large Language Models for Operational Training and Knowledge Integration

STTR · Phase: BOTH · Topic DON27TZ02-NV012 · Solicitation 27.TZ

The Department of the Navy faces increasing challenges associated with workforce modernization, technical skill development, personnel turnover, distributed operations, and the growing complexity of mission systems and operational environments. As experienced personnel retire or transition, critical institutional knowledge and operational expertise are frequently difficult to preserve, transfer, and scale across the workforce. Simultaneously, Navy training and workforce development communities must support increasingly divergent learning requirements across accession, technical education, and qualification pipelines. Existing training approaches often rely on static instructional materials, fragmented knowledge systems, and limited mechanisms for adaptive or individualized instruction. Commercial large language models (LLMs) demonstrate strong broad-domain reasoning capabilities but are fundamentally limited in their ability to reliably incorporate organization-specific knowledge without hallucination. They are unable to incorporate organization-specific terminology, mission procedures, technical constraints, and authoritative instructional content unique to mission environments. Additionally, these systems frequently generate responses without traceable references, limiting user trust and reducing suitability for technical learning and operational instruction. This gap creates a strong need for a specialized LLM system precisely tuned to an organization's corpus and operational requirements. The Navy seeks development of a hyper-parameterized, explainable LLM framework designed specifically to support mission-focused training and knowledge transfer. The system should enable personnel to learn new technical and operational topics through an interactive interface. Outputs should be grounded in authoritative manpower, personnel, training, and education (MPTE) documentation while also leveraging the broad contextual understanding available from foundation LLMs. The proposed capability shall support accelerated workforce proficiency development, preservation of institutional knowledge, and adaptive learning across operational, technical, and mission-support communities while providing transparent, auditable, and trustworthy instructional guidance tailored to user roles, experience levels, and training objectives. Explainability is a critical requirement for organization-specific LLMs operating in mission environments. The proposed solution must provide transparent and auditable reasoning pathways that allow users to understand how conclusions, recommendations, or generated outputs were derived. The proposed capability should function as an intelligent training and instructional assistant capable of: • Teaching mission-specific topics using authoritative organizational documents and instructional materials • Combining proprietary knowledge with broad-domain contextual reasoning from foundational LLMs • Guiding users through complex technical, operational, engineering, and/or procedural concepts • Adapting explanations and instructional depth based on user role, expertise, and/or training objectives • Providing explainable outputs with traceable references to source documentation • Reducing hallucination through structured retrieval and grounded response generation • Supporting continuous updates as training materials, policies, and mission guidance evolve

SBIR/STTR 은 미국 중소기업만 지원할 수 있습니다(Small Business Act 법정 요건). • 계열사를 포함해 상시 종업원 500명 이하 • 미국 시민 또는 영주권자 1인 이상이 50%를 초과해 직접 소유·지배 • 미국 내 사업장을 두고 주로 미국 내에서 사업을 영위할 것 • 수행책임자(PI)의 주된 근무처가 신청 기업일 것 출처: https://www.sbir.gov/faq/eligibility-requirements

Primary country

🇺🇸 United States

Also eligible

None

Foreign companies

No

Local entity

Required

Location condition

미국 내 사업장을 두고 미국 시민·영주권자가 50%를 초과해 소유한 중소기업만 신청할 수 있습니다(법정 요건).

Document requirements were not captured (needs verification). Check the official announcement.

Application process not captured.

Evaluation process not captured.

Process steps were not captured.

No risk assessment yet. Re-analyse from Admin.

No IP assessment yet.

Official URL

https://www.dodsbirsttr.mil/topics-app/

Application URL

https://www.dodsbirsttr.mil/topics-app/

Last fetched

2026.11.06

Human review

Not reviewed — check the official announcement

First seen

2026.11.06

Data status

Auto-collected, not reviewed

AI enrichment

Needs verification