PARRY LABS, LLC — Department of Defense STTR Phase I: SOCOM23B-001
PARRY LABS, LLC — STTR Phase I award from Department of Defense.
Phase I STTR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Defense in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $209,761. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code SOCOM23B-001 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $209,761
- Agency
- Department of Defense · Special Operations Command
- Program / Phase
- STTR · Phase I
- Topic
- SOCOM23B-001
- Solicitation
- 23.B
- NAICS
- —
- Place of performance
- MD
- Period
- 2023-08-07 → 2024-03-15
Description
Existing airborne defense systems integrate a wide variety of sensors necessary to provide operators with situational awareness across the visual, thermal, signals, and electromagnetic spectrums. To date, individual sensor systems have been largely stove-piped, as have Artificial Intelligence/Machine Learning (AI/ML) and advanced, Size, Weight, and Power (SWaP)-optimized data processing systems. The resulting challenge has largely precluded the reuse and feasibility of AI/ML solutions across platforms and sensors, resulting in fewer capabilities being deployed across SOCOM and DoD sensors at a higher cost per capability. To fill this gap, SOCOM requires portable, open architecture solution for orchestration of AI/ML at the edge that consists of three primary pillars: 1) an open architecture computing environment to provide computing resources to mange AI/ML from multiple sensors simultaneously; 2) a container-based software architecture to run AI/ML orchestrators and inference models against sensor data; and 3) a scalable AI/ML development and training pipeline and edge orchestration mechanism to transform cross-sensor data into actionable information.