# Satlyt Raises $8 Million to Expand AI Processing on Satellites

By John Kim

Canonical URL: https://www.tokenpost.com/news/business/31352
Published: 2026-10-13T16:47:32.000Z
Updated: 2026-10-13T16:47:32.000Z
Section: Business

> The seed funding will support hiring, additional deployments and software that analyzes spacecraft data before it reaches Earth.

Satlyt has raised $8 million in seed funding to expand software that processes data and runs artificial intelligence models on satellites, reducing the amount of raw information sent back to Earth.

The round was led by non sibi ventures. Satlyt will use the funding for hiring and additional spacecraft deployments as it develops software designed to work across satellites operated by different companies.

The Sunnyvale, California- and Nairobi-based startup was founded in 2024 by Rama Afullo, founder and CEO of Satlyt. Its software analyzes telemetry, imagery and system logs in orbit, allowing spacecraft to summarize or process information before transmission.

Satlyt has run Google DeepMind’s Gemma model onboard a satellite. In two diagnostic tests, the model reduced payloads from 1,319 bytes to 469 bytes and from 1,318 bytes to 464 bytes, representing reductions of 64.4% and 64.8%. The model generated results at 22.71 and 25.48 tokens per second in the tests.

“Our focus is on making onboard AI practical under real spacecraft constraints,” Nelson Kigen Psenjen, Satlyt’s chief technology officer, said.

“Space systems are one of the clearest examples of why local AI matters,” Afullo said.

Satlyt is pursuing a software-layer approach rather than building its own satellites. Its longer-term goal is to connect spacecraft into a shared orbital computing network, allowing computing resources to be used across multiple satellites.

Satlyt was selected for a Small Business Technology Transfer project focused on delay-tolerant mobile edge computing and content delivery networks in space. The project has an estimated technology-readiness progression from Level 2 to Level 4.