DOD SBIR 24.2 Annual

Release Date
April 17th, 2024
Open Date
May 15th, 2024
Due Date(s)
June 12th, 2024
Close Date
June 12th, 2024
Topic No.


Trusted, Generative AI for Acquisition Process Acceleration


Department of DefenseN/A


Type: SBIRPhase: BOTHYear: 2024


The Department of Defense (DOD) is seeking proposals for a research topic titled "Trusted, Generative AI for Acquisition Process Acceleration". The objective of this effort is to improve operational efficacy and enable better strategic positioning through the development of a customizable, AI-assisted software tool for acquisition processes. The tool should automate repetitive tasks, standardize document preparation, attain DoD Authorization to Operate (ATO) at sufficient security levels, and have a scalable and modular architecture. It should also speed up the acquisition process, track critical timeline events, and have user-friendly interfaces. This is a Direct-to-Phase-II (D2P2) topic, meaning no Phase I awards will be made. Phase II will involve further development of the technical approach and conducting relevant demonstrations. Phase III will focus on commercialization and transitioning the technology to government and civilian users. The technology keywords include Trusted AI, Generative AI, natural language processing, and workflow automation. The solicitation is open until June 12, 2024. For more information, visit the solicitation link.




The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.


OBJECTIVE: AFRL faces the challenge of current acquisition and contracting methods being rooted in manual processes and failing to deliver at the speed of need as a result of misalignment, errors, and inconsistencies. To keep up with the rapidly changing technological landscape and new threat dynamics, streamlined, AI-assisted acquisition processes are essential. The objective of this effort is to improve operational efficacy to enable better strategic positioning via a customizable, AI-assisted software tool.


DESCRIPTION: A trusted, vetted AI-assisted, automated acquisition workflow for FAR contracting should include the following aspects to meet the stated objective: (1) must include automation of repetitive tasks reserving human resources for decisional and/or complex tasks; (2) must be able to attain DoD Authorization to Operate (ATO) at sufficient security levels to meet contracting needs (Controlled Unclassified Information/Impact Level-5); (3) must standardize document preparation with built-in compliance for regulatory requirements to reduce errors and inconsistencies in acquisition document preparation; (4) must have scalable, modular architecture and be customizable to meet the growing demands of multiple organizations with unique needs; (5) must quantifiably speed up the acquisition process by reducing time-to acceptance, by reducing rework rate, and by reducing overall person-hours spent in pre-contracting document preparation; (6) must track critical timeline events and allow for workflow interruption/reprioritization; and (7) must include simple, user-friendly, adaptable interfaces for program managers, contracting officers/buyers, and management.


PHASE I: As this is a Direct-to-Phase-II (D2P2) topic, no Phase I awards will be made as a result of this topic. To qualify for this D2P2 topic, the Government expects the applicant(s) to demonstrate feasibility by means of a prior “Phase I-type” effort that does not constitute work undertaken as part of a prior or ongoing SBIR/STTR funding agreement. This includes determining, insofar as possible, the scientific and technical merit and feasibility of ideas appearing to have commercial potential. It must have validated the product-market fit between the proposed solution and a potential AF stakeholder. The offeror should have defined a clear, immediately actionable plan with the proposed solution and the AF customer. The feasibility demonstration should have; (1) Demonstrated a Phase I-like/prototype software tool that uses customizable, trusted AI; (2) Identified the prime potential AF end user(s) for the non-Defense commercial offering to solve the AF need, i.e., how it has been modified; (3) Demonstrated ability to attain DoD ATO on similar products; and (4) Described if/how the demonstration can be used by other DoD or Governmental customers.


PHASE II: Under the phase II effort, the awardee(s)shall sufficiently develop the technical approach, product, or process in order to conduct a two relevant demonstrations using distinct contracting action types. Identification of business model modifications required to further improve product or process relevance to improve efficiency, compliance, and consistency should be documented. These Phase II awards are intended to provide a path to commercialization, not the final step for the proposed solution.


PHASE III DUAL USE APPLICATIONS: The awardee(s) can expect to pursue commercialization of the various technologies developed in Phase II for transitioning expanded mission capability to a broad range of potential government and civilian users and alternate mission applications. Direct access with end users and government customers will be provided with opportunities to receive Phase III awards for providing the government additional research & development, or direct procurement of products and services developed in coordination with the program.




KEYWORDS: Trusted AI; Generative AI; natural language processing; workflow automation

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