TECHNERGETICS, LLC — Department of Defense SBIR Phase II: AF193-DCSO1
TECHNERGETICS, LLC — SBIR Phase II award from Department of Defense.
Phase II SBIR prototype / development signal
- Phase II is where Department of Defense funds deeper R&D after feasibility. Incumbents with Phase II history are serious competitors on adjacent topics.
- Use this award as past-performance context and to map customer organizations for STRATFI/TACFI-style transition planning.
- Obligated amount $1,499,999 is consistent with substantial Phase II-scale effort; compare to related awards from the same agency.
- Topic code AF193-DCSO1 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $1,499,999
- Agency
- Department of Defense · Air Force
- Program / Phase
- SBIR · Phase II
- Topic
- AF193-DCSO1
- Solicitation
- DoD SBIR X19.2
- NAICS
- —
- Place of performance
- NY
- Period
- 2020-06-16 → 2022-09-16
Description
InfinityAI will operationalize AI with an accredited AI/ML-focused architecture to overcome current DoD Enterprise technical limitations and enable meaningful human-machine teaming. This ecosystem is a baseline for AI superiority, which bonds data scientists, algorithm developers, commercial technology, and warfighter to achieve mission success. InfinityAI helps identify mission requirements in need of AI support. It collects necessary data via an open connector API, feeding to a robust AI data repository, currently missing from the DoD Enterprise. This repository manages the collection of raw and labeled data, provides access for algorithm development, and eases model validation. Operationally relevant data empowers data science to maximize algorithm accuracy before deployment. AI suffers from limited paths to operations. This hinders the warfighter and data scientist, results in unattainable benefits, and deters market competition from advancing operational AI models. InfinityAI overcomes this by providing a process for staging, refining, and deploying algorithms while factoring in security, performance, and accuracy checkpoints. This ensures that models transitioned into operations provide accurate, expected impact. Users leverage a metric dashboard to explore algorithm performance, and user feedback is captured, encouraging crowdsourced methods to increase algorithm relevancy and accuracy. This prevents operational AI “black boxes,â€Â instead providing a collaborative, beneficial experience.