SBIR/STTR Research Award

Deep Learning Platform for Real World Natural Language Processing (NLP) and Search

Award identifier ee5a3b2d046f071774c4f6e3acab595035b2505476e22a1196989b99da5d6009Awarding agency Department of Defense · Air Force

Award amount

$749,997

Program and phase

SBIR · Phase II – Direct to Phase II

Award year

2022

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Award details

Awarding agency
Department of Defense
Branch
Air Force
Recipient company
CLARIFAI, INC.
Proposal award date
2022-01-19
Contract end date
2023-04-18
UEI
LEDKTXJMC9D7
Solicitation number
X21.1
Topic code
AF211-DCSO1

Research abstract

Clarifai's commercial platform (TRL-7) for machine-learned (ML) Computer Vision (CV) will provide the ability to apply state-of-the-art Artificial Intelligence (AI) techniques for improving mission readiness and sustainment, particularly in the area of Natural Language Processing (NLP). Clarifai is a world leader in the field of Computer Vision (CV), and its employees have published in many peer-reviewed venues garnering tens of thousands of citations. We have a proven track record of adapting our commercial solutions for government purposes, as shown with our participation in Project Maven and our work with other government clients. The People's Liberation Army (PLA) assumes we cannot read their language, and they are correct. This is primarily due to limited Mandarin linguists, long training pipelines, and a lack of high TRL technologies capable of providing real-time exploitation of  Mandarin, Smart Cities, and A2AD platforms. To counter & exploit, AFSOC will need to develop a seamless UX Platform for end-users requiring best-in-breed natural language processing (NLP). Clarifai's AI tech stack helps tag, analyze, and process the translated data sources and speed up targeting cycles. The benefits of using machine learning on top of best-in-breed NLP will help Analysts speed up the intent behind contextual data and language.  Additionally, allowing Clarifai to work on the front end and back end UX, intel communities, and operators can significantly reduce workforce, speeding up targeting cycles.  With AI-powered automated metadata generation, improving cataloging descriptions and asset searchability will help build intelligent searches based on objects, people, color, emotions, and even demographic characteristics. With rich metadata tagging, intel communities and operators will be able to understand not just what's in assets but also how it's used, where it's used, who's viewing it, and their relationships with each other. With a single AI platform, users are given the ability to build and train custom AI models at scale and establish workflows to manage the metadata generation process.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

10 SBIR/STTR records share this UEI (2020–2025). Reported award amounts total $5,462,743. Reported amounts are not federal obligations.

Phases: Phase I, Phase II – Direct to Phase II, Phase II

Agencies: Department of Defense

Agency intelligence

Department of Defense

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