SkyServe is an AI & edge computing technology company for satellites platforms, helping satellite operators to process data onboard to reduce their operational costs, latency and enabling them to deliver essential data products to their customers in near real-time.
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Starting in 2019, we struck off numerous ideas ranging from reusable rocket kits, to geospatial analytics for defense & intelligence services.
In 2020 we founded SkyServe with an ambitious mission to bring a large-scale ‘autonomy stack’ for all spacecraft, starting with Earth Observation satellites in LEO, with an ultimate objective to unlock the next computing revolution from space!
Sprung from TeamIndus, joint winners of Google Lunar XPRIZE, and after having poured their hearts into the mission over 7 years, post conclusion of competition, it was time for 3 key members of the team to move on and look to solve for impactful problems with ingenuity.
Vinay, Co-founder & CEO
Experience across leadership roles in fortune 100.
Led business at Team Indus
Enterprise growth at IBM
Revenue assurance at GE
Charting the course of building value streams for sustainable growth at SkyServe.
A space enthusiast and an ardent fan of deep impact solutions for sustainable world, ardent believer in chasing the impossible to make possible.
[LinkedIn]
Vishesh, Co-founder & CTO
B. Tech Aerospace Engg (IIT Kanpur, India)
2 years at IBM
6 years at TeamIndus [Lunar XPRIZE]
Building SkyServe platform, the edge computing infrastructure for satellites for Earth Observation and beyond. He led the Guidance, Navigation and Control team at TeamIndus building autonomous lunar descent capabilities for a lunar lander. He is interested in pushing future-looking space research projects.
Adithya, Co-founder & CPO
B. Tech Aerospace Engg (UPES, India)
MSc Astronautics & Space Engg (Cranfield, UK)
6 years at TeamIndus [Lunar XPRIZE]
Developing systems & crafting use-cases for drones, CubeSats and Smallsats since 2010. A Systems Engineer by training, Geospatial Product Designer by choice & a poet at heart.
Today, Earth Observation satellites generate Petabytes of data daily. However, about 70% of this data is unusable due to atmospheric factors like cloud cover and other noise. Annually, satellite operators spend about $2M per satellite to downlink just this unusable data. Without even factoring the time, efforts and resources to process it further.
Even when the captured scene might have cloud cover well within the acceptable threshold, the customer’s Area of Interest (AoI) within the scene might be invisible exactly under the small portion of clouds. Causing the satellite operators to service multiple repeat tasking requests.
Furthermore, in critical scenarios like national security, climate-induced disasters or highly perishables on-ground situations, these failure points and the latency involved processing satellite data could be catastrophic.
We are on a mission to make satellite data processing smart and sustainable. By adding an intelligence layer onboard, we are making the satellites smarter.
This layer is our onboard AI and Edge Computing Platform, STORM. That equips the satellite with an array of applications that selectively process non-cloudy parts of the images focusing on the customer-defined AoIs.
Furthermore, the operator can host GeoAI models on this platform, to generate insights in real-time for variety of use-cases, which can be seamlessly disseminated over inter-satellite links and onto low-power handled devices on the ground.
Through our proven satellite edge computing software and infrastructure deployed across EO satellite constellations, we are stitching together a multi-sensor inferencing capability, over multi-orbit capacity , abstracting out the data diversity and enabling satellites operator to serve end-users and intelligence practitioners to deploy their AI to run in-situ & continuously, downlinking, what they need, when it matters.
We are partnering with satellite operators, each specializing on specific sensing modalities and expanding their constellation’s coverage and revisit,