CelestCore helps satellite operators turn collision risk data into defensible maneuver decisions, including knowing when the data itself should not be trusted.
Satellite operators receive hundreds of collision alerts every week. Each one must be evaluated because the consequences of getting it wrong are permanent. An unnecessary maneuver wastes fuel and shortens mission life. A missed threat can destroy an asset.
Existing systems deliver tracking data and risk metrics, but they stop at the operator's screen. They do not assess whether the data is trustworthy. They do not recommend specific actions.
The bottleneck is no longer collecting orbital data.
The bottleneck is making the decision.
Operators already wait 24 to 36 hours on most alerts because the majority resolve naturally as tracking improves. The decisions that matter are the ones where waiting no longer helps. No existing tool tells operators when they've crossed that line, or when the data they're looking at can't support any decision at all. The May 2024 Gannon storm showed exactly this: operators acted on data that looked normal but no longer reflected reality.
NASA's own satellites get a dedicated team of analysts who review every close approach, run multiple computation methods and advise on decisions. Commercial operators get a data message with one number and no context. CelestCore brings that analytical rigor to operators who don't have it. Delivered as software, not a team of analysts.
The system ingests conjunction data from existing providers, assesses data quality, optimizes maneuver options across fleet constraints and delivers confidence-scored recommendations with full traceability.
Analyzes how uncertainty signals evolve across successive data updates. Detects when risk data is physically inconsistent. Tracking gaps, estimation resets, atmospheric model failures. All flagged before any decision is made.
Evaluates maneuver options across an entire constellation simultaneously. Fuel budgets, mission schedules, overlapping alerts. All weighted by how much the system trusts each conjunction's data.
Every recommendation passes through a separate safety verification path that cannot be bypassed or influenced by the advisory system. When confidence is insufficient, the system formally declines to recommend rather than producing an answer the data cannot support.
A view into the decision workflow an analyst uses day to day. The fleet view below shows a typical workday for an operator running 80 satellites.
CelestCore does not execute maneuvers. Every recommendation is advisory and every override is logged. The operator is always the one in command.
The analyst works through three events while the system resolves the other thirty-seven on its own. The same pattern holds from 80 satellites to several thousand.
Validated against NASA CARA's published benchmark. Real conjunction events involving Hubble, TERRA, AQUA, ICESat-2 and other high-value assets. Results identical to 14 significant digits.
Three CDM updates all reported effectively zero risk using the standard 2D method most operators rely on. CelestCore detected physically inconsistent uncertainty evolution and flagged the data. NASA's higher-fidelity Monte Carlo calculation showed the actual risk was roughly 100,000x higher.
CelestCore is developed by a founder with background spanning machine learning systems, orbital data analysis and decision system architecture.
CelestCore was born from a pattern that I often noticed. Many maneuver decisions were being made on data that looked clean but was not. The tools operators had were not built to tell them the difference. What they wanted was not more alerts or prettier dashboards. They wanted to know when to trust the number in front of them; that question was not being answered anywhere in the stack. As such, I built CelestCore as an answer and at a cost and commitment level that fits operators who do not have a dedicated analyst team standing behind every conjunction.