As satellite constellations grow and Earth observation data surges, the need for smarter, faster, and more efficient space-based systems has never been greater. SkyServe is leading this transformation with its in-orbit AI platform, enabling satellites to process data, detect events, and deliver actionable insights directly from space. In this interview, Vishesh Vatsal, Co-Founder & CTO of SkyServe, explains how their EdgeAI Suite is revolutionizing satellite intelligence, reducing latency, and redefining sustainability in orbit—from wildfire alerts to maritime monitoring.
Q: How is SkyServe enabling a new model of AI-driven intelligence in space, and what makes this approach different from traditional satellite operations?
Traditional Earth Observation missions depend on downlinking vast volumes of raw data for ground-based processing. This results in long delays, soaring bandwidth costs, and a flood of unusable or irrelevant imagery. SkyServe disrupts this model with STORM, our in-orbit compute platform that allows users to deploy and run their own AI models directly onboard satellites. By pre-processing data in space, STORM can discard cloud-covered or low-quality images, prioritize high-value content, and deliver near real-time insights to users on the ground. This shift from raw data delivery to actionable intelligence transforms satellite operations, making them faster, leaner, and far more responsive to dynamic needs on Earth.
Your work reflects a broader shift toward decentralised intelligence in orbit. What does this mean for how space-based data is processed and used?
This shift is part of a larger evolution toward the intelligent edge, extending compute power closer to where data is generated. In space, that means satellites no longer act as passive cameras but as autonomous decision-makers. With onboard AI running through platforms like SkyServe’s STORM, satellites can process, filter, and act on data in orbit. They don’t just observe a wildfire, they recognize it, classify it, and trigger alerts within minutes.
This decentralised architecture eliminates reliance on overburdened ground stations, reduces latency, and unlocks a future where fleets of satellites operate as a real-time, intelligent mesh, monitoring Earth continuously, adapting dynamically, and accelerating response times across domains like climate monitoring, defense, disaster relief, and maritime security. It’s the difference between waiting for insight and having it delivered the moment it’s needed.
Q: Can you explain what ‘orbit-aware processing’ means in your system, and how it helps satellites make smarter, context-driven decisions?
Orbit-aware processing gives satellites dynamic situational awareness. Instead of running static models, SkyServe’s EdgeAI Suite adapts onboard intelligence based on the satellite’s real-time location, altitude, and mission context. For example, as a satellite moves from dense forests to coastal waters, it can autonomously switch from wildfire detection to ship tracking, activating only the models relevant to its current environment. This ensures that compute resources are used efficiently, power is conserved, and the insights delivered are always aligned with mission priorities. It’s how we turn satellites from passive sensors into responsive, context-aware agents in orbit.
Q: What kind of compute hardware and architecture runs SkyServe’s EdgeAI Suite onboard? How do you ensure reliability in the constraints of space?
SkyServe’s EdgeAI Suite operates on modular, space-grade compute systems compatible with over 60 satellite platforms. These systems use radiation-tolerant processors and are built with redundancy and fault-tolerance in mind. Our software layer includes self-healing processes and real-time monitoring, ensuring consistent performance despite harsh space conditions.
Q: How is data managed onboard, what gets stored, what’s filtered out, and how does this improve efficiency across the satellite network?
SkyServe’s onboard platform manages data through a smart, modular pipeline that combines pre-processing, filtering, and AI inferencing directly in orbit. Raw data from sensors is first cleaned and calibrated (de-hazing, noise removal, georeferencing). Then, using Smart Xtract, the system automatically discards data that doesn’t meet mission-defined criteria—such as excessive cloud cover, poor-quality pixels, or irrelevant scenes over the area of interest.
Only valuable, insight-rich outputs are retained and downlinked, often reduced to lightweight metadata or processed images. This minimizes bandwidth consumption, reduces storage load, and accelerates ground delivery. The result is a leaner, more efficient satellite network that can focus resources on high-value insights instead of transmitting terabytes of redundant data.
How do AI models get deployed and updated once they’re in orbit? What kind of adaptability does your platform offer post-launch?
AI models are pre-loaded before launch but can be updated securely via uplinks post-launch. This allows operators to adapt to new mission requirements or leverage advancements in AI without launching new satellites. Our platform supports iterative development and long-term operational flexibility.
Before launch, AI models are rigorously tested in lab environments that closely simulate orbital conditions. Once optimized, they’re deployed onboard satellites via our STORM platform. But adaptability doesn’t stop there. STORM supports secure post-launch uplinks, allowing operators to update or swap models as mission needs evolve or as new AI capabilities emerge. This means satellite missions are no longer frozen at launch,they can learn, improve, and respond dynamically over time, just like the environments they monitor.
Q: What are some real-world applications where onboard AI and orbit-aware insights have delivered value, such as in maritime tracking or environmental monitoring?
SkyServe’s platform has already powered missions like Matterhorn, which used onboard AI for vegetation classification and environmental analytics. In partnership with NASA’s Jet Propulsion Laboratory, we supported the deployment of AI models from NASA’s New Observing Strategies SensorWeb project. These models enable near-real-time monitoring of wildfires, floods, urban heat islands, and other environmental phenomena for both disaster response and scientific research. By analyzing data directly in orbit, SkyServe dramatically shortens the time between observation and insight, helping decision-makers act faster, more efficiently, and with lower operational burden.
Q: Looking ahead, how do you see in-orbit AI shaping the future of Earth observation and satellite data infrastructure over the next five years?
In-orbit AI is poised to fundamentally shift Earth observation from a data delivery paradigm to an insight delivery model. Over the next five years, we anticipate a dramatic reduction in the need for raw data downlinking. Satellites will increasingly act as autonomous agents—identifying events like wildfires, emissions, or illegal maritime activity on their own, and triggering real-time alerts.
This will lead to faster decision cycles, more agile missions, and lower ground infrastructure dependency. AI will enable onboard learning, anomaly detection, and dynamic tasking based on context. The satellite data infrastructure will become increasingly decentralized and software-defined, with edge AI enabling smarter constellations, better data curation, and lower operational costs.
Q: How does SkyServe’s approach contribute to sustainability in satellite operations?
SkyServe improves sustainability on two fronts, operational efficiency and planetary impact. By filtering redundant data onboard and activating compute only when needed, we reduce energy use, lower downlink demands, and minimize reliance on ground infrastructure. But the greater impact comes from enabling real-time, orbit-aware applications like early flood detection, emissions monitoring, and climate risk assessment. These capabilities support climate resilience, disaster response, and environmental compliance. Our platform aligns with a future of space that is low-latency, low-carbon, and high-impact, where satellites are not just observers, but active agents of planetary stewardship.
Q: What makes SkyServe’s EdgeAI Suite different from other onboard AI solutions in the market?
SkyServe’s EdgeAI Suite stands out for its mission-proven reliability, full-stack flexibility, and ease of integration. Unlike single-function onboard processors, our system combines STORM (flight software for pre-processing and inferencing) with SURGE (a developer platform to build, test, deploy, and manage models at scale).
We are one of the few onboard AI providers with heritage across multiple missions. Mission Matterhorn with D-Orbit demonstrated cloud and vegetation classification in under 60 seconds, reducing downlink by over 5X. Our mission with NASA’s Jet Propulsion Laboratory validated our platform for object detection and AI model execution in real-world orbital conditions. These missions prove that SkyServe is space-tested, trusted, and built for scalable, commercial deployment. Our stack is sensor-agnostic, hardware-flexible, and compatible with commercial edge processors—ready to empower optical, hyperspectral, SAR, and IoT payloads.
Q: Can you share a bit about SkyServe’s founding story and the expertise behind its technology?
SkyServe was founded to make space systems smarter and more responsive. Our team combines expertise in AI/ML, aerospace engineering, and satellite systems. This background ensures our solutions are both technically advanced and practical for real-world space operations and on-ground applications.
Q: What is SkyServe’s outlook on the market for AI-driven satellite services, and where do you see growth opportunities?
The market for in-orbit AI services is expanding rapidly. Earth observation data is projected to exceed 2,000 petabytes annually by 2030. SkyServe addresses this gap by delivering onboard processing solutions that unlock more value per satellite. Growth opportunities are strongest in maritime monitoring, disaster response, and urban analytics. Our focus is on building scalable, adaptable solutions that evolve with industry needs.
The market for AI-driven satellite services is rapidly expanding as Earth observation shifts toward real-time intelligence and insight-first delivery. SkyServe sees strong growth in:
● Defense and surveillance, where latency is mission-critical.
● Climate and disaster resilience, including wildfire, flood, and emissions monitoring.
● Energy and infrastructure, especially in solar, transmission, and remote asset monitoring.
● Precision agriculture and insurance, where fast, localized insights enable smarter decisions and payouts. As more satellite constellations go up, the demand for in-orbit intelligence will outpace raw image delivery. SkyServe is positioned to be the AI operating system for next-gen constellations, enabling satellites to think, act, and deliver value autonomo
