Data & Insights

• Construction orders with values
• Mission, sector, orbit, age, status
• Anomalies and capacity lost
• Payloads, capacity, and subsystem data
• Satellite bus reliability data

• All orbital launch attempts
• Relevant sub-orbital launch attempts
• Launch vehicle specs and histories
• Launch orders and values
• Future planned launches

• Reliability and failure trends for launch vehicles and spacecraft buses
• Historical insurance claims and loss values
• Spacecraft and launch vehicle values

• Company reports including financials
• Company structure and key personnel
• Communication frequencies and links to ITU filings
• Spacecraft fleet overviews by company

Take control of your conjunction screening schedule by leveraging our high-accuracy custom screening services. Just launched?  No problem! Our ephemeris screening service in Slingshot Beacon can generate CDMs based on both your catalogued and uncatalogued objects.

Enhance your understanding of any given conjunction by leveraging our space object tracking service. Resulting data products, such as raw observations, OD states, predicted ephemerides, and CDMs, can be made available via our Slingshot Platform APIs.

Provide your GNSS telemetry to our Orbit Determination-as-a-Service system to generate orbit determination states and predicted ephemerides. Ephemerides can then be used in Slingshot Beacon, or input to our conjunction screening service, to screen for conjunctions and evaluate the associated risk.

Understand how any satellite has been maneuvering with maneuver history, drift history, longitude history, station keeping changes, and flagged behavior change – and be the first to know when there is a change.

Understand the mission phase of every satellite and be the first to know when its mission phase changes. Phase Change reveals long-term patterns and events of interest, including: Pattern of life characterization, changes of object status from active to inactive... or vice versa, and shifts to disposal orbits.

Keep tabs on the satellites operating near your spacecraft. Receive reports on their mission phase and behavior over time, allowing for fast and accurate response and risk mitigation when issues arise.

Discover satellites that exhibit similar behavior patterns, based on global information and fine-grained maneuver and orbital analysis. Find unexpected similarities in pairs of objects, help to anticipate potential actions of unknown satellites based on similar known satellites, and improve awareness on general maneuver patterns of groups of objects.

Pattern of life insights can be quickly and easily pulled into reports detailing the behaviors exhibited by a satellite over its lifespan. Operators can easily access all available information in a single package for better space situational awareness.

Stay on top of satellite activity around your craft with drift detection, inspector satellite detection, and future target prediction for stalker satellites.

Be informed of the most interesting activity happening in space with our prioritized High-Interest Watchlist, which provides near real-time updates on new or interesting activity alongside detailed reports and analytics.

Receive alerts when satellites in your neighborhood exhibit new and unexpected behaviors, improving safety and providing transparency into activity around your spacecraft.

See how a satellite’s neighborhood changes over time by examining the history of satellites in the neighborhood, distances between satellites, past maneuvers, and past behaviors. Improve comprehension of alerts in the present day with easy comparison to similar historical events.

Receive automated reports detailing characteristics of neighborhoods around the GEO belt, along with common shifts in behavior and maneuver patterns, improving your GEO space domain awareness.

Slingshot’s algorithms identify anomalous satellite behavior through parallelized analytics including inverse reinforcement learning and unsupervised machine learning models.

Outlier Spacecraft Insights can efficiently search a vast space of tens of thousands of correlations against millions of data points.  It has been tested against 600M+ real and simulated data points in 35+ scenarios.

Machine learning algorithms extract relevant features with respect to each individual constellation. This allows metrics to be calculated for each individual satellite in the context of its larger collective, facilitating the identification and characterization of disparate members and behaviors.

Slingshot's algorithms have built-in explainability that indicates why a specific satellite's behavior has been flagged as an outlier.

Real-time data monitoring services are available to continuously analyze and monitor known and unknown RF signals to detect and characterize anomalies.

Perform retroactive RF interference investigations informed by empirical measurements where interference sources are identified, geolocated, categorized, and indexed for lifecycle characterization.

Slingshot utilizes cutting-edge computer-aided design (CAD) models, material attribution, and high-fidelity physics modeling to generate interactive 3D visualizations, giving partners the tools they need to comprehend and visualize the complex RF environment.