On March 10, 2024, iGO submitted its first federal research proposal: a NASA STTR Phase I under subtopic T10.05, “Integrated Data Uncertainty Management and Representation for Trustworthy and Trusted Autonomy in Space,” titled Intelligent Space Resource Prospecting System. The proposal was a partnership with Oral Roberts University (Dr. Robert Leland as Principal Investigator, with Dr. Andrew Lang and Dr. Xiaomin Ma), and its aim was the thing iGO cares about most: turning the world’s public asteroid data into a ranked answer to the question, where is the water, and which rocks can we reach?
On June 11, 2024, NASA informed us the proposal was not selected. That year NASA received more than 1,500 Phase I proposals across the SBIR and STTR programs and selected 299: 260 SBIRs and 39 STTRs. Ours was one of the STTRs that did not make it.
What the reviewers told us
We asked for the reviews, and we read them the way they were meant to be read.
The reviewers found the science plausible and the team qualified. What they could not find was specificity. The proposal described machine learning and deep learning without naming architectures, inputs, or outputs. It promised to aggregate existing databases without saying what already existed or what those databases had already concluded about water. It allocated hours but no budget. It listed customers but no market analysis. One line stayed with us: “All information provided is at a very high level. The significance of the work cannot be determined.”
They were right.
What we did with it
A rejection with honest reviews is worth more than a polite silence. We stopped describing a prospecting system and started building one.
The result is iSEE, the intelligent Space Exploration Engine, the first stage of the iGO fleet and the only one that is operational today. It ingests the public asteroid observation streams the proposal only named, maintains an append-only ledger of every observation it has ever seen, and re-scores the population continuously by accessibility, expected water content, and return-window timing. It runs on iGO’s own infrastructure, every night, whether or not anyone is watching.
iSEE is not in its final form. But it exists, it runs, and it answers the reviewers’ question about significance with a system rather than a paragraph. Its function is public; its findings are the company’s.
Thank you, ORU
Dr. Leland, Dr. Lang, and Dr. Ma gave real time and real thought to a small company with a large idea, and the proposal’s shortcomings were ours to own, not theirs. iGO is grateful for the collaboration and welcomes the chance to work with Oral Roberts University again, next time with a working engine to point at.
We will submit again.