CVISION AI INC — Department of Commerce SBIR Phase II: 9.3.02
CVISION AI INC — SBIR Phase II award from Department of Commerce.
Phase II SBIR prototype / development signal
- Phase II is where Department of Commerce 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 $398,483. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code 9.3.02 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $398,483
- Agency
- Department of Commerce · National Oceanic and Atmospheric Administration
- Program / Phase
- SBIR · Phase II
- Topic
- 9.3.02
- Solicitation
- NOAA-OAR-OAR-TPO-2020-2006595
- NAICS
- —
- Place of performance
- MA
- Period
- 2021-02-01 → 2022-07-31
Description
During phase I we created a web-based media analytics platform, Tator, as a foundation for the exploration, enrichment, and evaluation of underwater video and imagery. Tator supports collaborative annotation, customizable metadata, advanced playback features, third-party programming interfaces, and registering and running algorithm workflows. In phase II we will expand the core capabilities of Tator and introduce libraries of algorithms and media in conjunction with project partners and related efforts such as FathomNet, the MIT Open Ocean Initiative, and the Ocean Discovery League. This will enable enrichment of new and existing underwater media through automated annotation and provide researchers tools to curate and transform media into algorithm training data. The core Tator platform will be matured into enterprise-grade software, enabling in-app analyses and reports, advanced annotation, task management features, and standards compliant project templates. To improve commercial viability of our cloud-based managed offering, Tator Cloud, we will optimize our cloud architecture to reduce costs. To facilitate annotation during data collection efforts, as well as future real time algorithm deployment, a ruggedized "Tator-in-a-box" module for use on local area networks will be created to help curation and annotation of dive data in real time, further reducing lag between data capture analysis.