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On-orbit AI-driven Automation for Range Characterization Analysis
AFWERX · AFWERX TACFI · AFWERX
AI-Readiness Score
19/25
Pathway Speed
5/5
Timeline Realism
3/5
Problem Framing
3/5
AI / ML Fit
5/5
Award + Transition
3/5
Award
$1,900,000
Award ceiling
WALLAROO LABS INC
Awardee
Posted January 6, 2025
Description
The purpose of the Orbital Prime research leading into this TACFI effort was to prove in a lab that the Wallaroo software platform could deploy, manage, and maintain satellite models at the edge on satellite hardware. This TACFI extension of the Phase II takes this research and development out of the lab and onto the satellite itself. This R/R&D effort will allow for the deployment and rapid iteration of artificial intelligence and machine learning (AI/ML) directly on-orbit, significantly enhancing United States Space Force (USSF) mission capabilities within the space domain.
Score Rationale
AFWERX TACFI is a top-tier fast-track OT instrument earning full marks on pathway speed, and the core problem — deploying and iterating AI/ML models directly on satellite hardware at the edge — is genuinely AI-shaped with a named platform (Wallaroo) and real prior Phase II work grounding it. The problem framing scores mid-tier because success criteria and data availability for on-orbit range characterization analysis are not explicitly defined in the description, leaving meaningful scope ambiguity; the $1.9M ceiling and implicit Phase III/production transition via USSF mission pull land it in the middle on award and transition. Timeline realism is assessed as tight-but-achievable given the unknown response deadline and the complexity of on-orbit deployment work.