What the buyer needs
AI summaryThe Department of the Air Force seeks market research information on the utility of Super Intelligence and Machine Learning software to support autonomous space vehicle operations in the cislunar environment. The scope focuses on evaluating existing, flight-demonstrated commercial off-the-shelf or near-COTS capabilities for autonomous station-keeping, trajectory optimization, hazard avoidance, computer vision, and autonomous maneuver planning. The required delivery includes executable software baselines validated in software-in-the-loop environments, with initial software delivery requested within 90 days of authority to proceed. Only onshore United States organizations are eligible to respond. Interested parties must submit electronic responses of no more than five pages in PDF format by 12:00 noon EST on December 4, 2026, using the subject line "DAF Cislunar Space SI – [Respondent Name]". Respondents may submit questions via NIPR until 12:00 noon EST on November 21, 2026. Submissions must include rough order of magnitude cost estimates and high-level delivery schedules, though the request for information does not commit the government to award a contract.
Scope of work
- Autonomous Mission Management to plan, sequence, or re-task vehicle actions during extended periods without ground contact
- Predictive Maintenance and Fault Isolation on-board anomaly detection systems for hardware faults and degradation
- On-board Trajectory Optimization for autonomous maneuver planning and propulsion management without ground-station intervention
- On-board computer vision multi-model image processing and identification of celestial bodies and man-made objects
- Initial executable software delivery and validation in a software-in-the-loop environment within 90 days of ATP
- Fully qualified flight software delivery by ATP plus 18 months integrated into Government furnished SV hardware
- Assessment of hosting hardware compute resources, power requirements, memory footprint, and processor architecture
- Verification and validation approach including safety bounds, fallback modes, runtime monitoring, and model update concepts
How proposals are evaluated
| Criterion | Weight / scoring |
|---|---|
| Technical implementation risk with technical approach and CONOPS, including technical maturity | Not specified |
| Risk associated with the schedule for delivery of validated flight qualified software | Not specified |
| Risk associated with prior domain expertise in cislunar SV operations and SI/ML implementation | Not specified |
| Respondent or respondent team overall ability to execute the program | Not specified |
| Risk associated with maturity of SI/ML models, algorithms, and software from prior demonstrations | Not specified |
Minimum qualifications
- Recent (within the past three years) experience in development and on-orbit operation of autonomous spacecraft flight software
- Design, development, and application of Super intelligence / Machine Learning (SI/ML) to space operations
- Design, development, and operation of classified space systems (preferred, not required)
Documents to include with your response
- Cover page with company name, primary and secondary POC details, and submittal date
- Description of recent relevant experience within the past three years
- Proposed technical approach addressing chosen focus areas, compute resources, maturity, and prior demonstrations
- ROM cost estimate for licensing, adaptation, integration, and demonstration of existing software capability
- Credible high-level schedule for delivery, validation, and flight qualification
How to submit
- Method
- Electronic via email
- Instructions
- Send electronically to specified POCs using subject DAF Cislunar Space SI – [Respondent Name]. Prepared in Microsoft Word 2016 or PDF format, maximum 5 pages, 12-point proportional font. For classified information, contact POCs for alternative network instructions.