Software-Defined
Intelligence for
Satellite Downlinks

Adaptive space-to-ground communication solution to recover more usable data for every satellite pass.

Mission

We're building the intelligent software layer
for satellite-to-ground communications.

LEO satellites are generating more data than today's static links can handle. The downlink is constrained at both ends — fixed waveforms on the spacecraft, hardware-bound receivers on the ground. DeepBro puts cognitive, adaptive software on both sides of the link, maximising usable downlink as conditions change in real time.

The DeepBro solution

From signal to usable data, in real time.

DeepBro adds an AI software layer across the satellite-to-ground link, running onboard the satellite and at the ground station — adapting continuously to changing conditions during each pass, so you recover more data without replacing hardware on either side.

Real-time adaptation

Optimizes mid-pass as signal conditions change.

Hardware-agnostic

Works with existing satellites, antennas, SDRs, and GSaaS platforms.

Edge + Cloud

Low-latency AI in orbit and at the antenna, managed through cloud SaaS.

Platform capabilities

AI-defined radio for the satellite-to-ground link.

DeepBro's platform starts with a cognitive radio engine that runs onboard the satellite and at the ground station, letting both sides adapt to changing signal conditions in real time. Built on the same AI-defined software foundation, the platform expands toward antenna aggregation — a breakthrough approach to scaling capacity without the cost and complexity of traditional hardware-heavy systems.

Current MVP

Cognitive Radio Engine

Software-defined signal intelligence that runs onboard the satellite and at the ground station, letting both sides adapt to changing link conditions in real time — improving signal usability and reception during every pass.

  • Automatic Modulation Recognition

    Identifies modulation from raw IQ data in real time.

  • AI Denoising

    Improves usable signal quality under noisy or degraded conditions.

  • Interference Detection

    Detects signal anomalies and unwanted RF interference.

  • Anti-Jamming / RF Resilience

    Supports adaptive operation in unstable or contested RF environments.

Validated on live satellite signalsDeployed with pilot customerShared inference engine — onboard and on the ground

Antenna Aggregation

DeepBro coordinates geographically distributed, low-cost antennas into a single virtual ground station — from the diversity combining running in production today to the AI-coherent aggregation in our R&D program.

Live today

Distributed Diversity Combining

Diversity combining across separated ground stations — the clean frames from every antenna are merged into one result better than any single antenna produced.

Breakthrough R&D

AI-Coherent Signal Aggregation

Deep learning synchronizes, weights, and sums raw signals across stations — commodity antennas performing like a single high-gain dish.

Dual node antenna aggregation — live from our network

METEOR-M2 3 137.9 MHz LRPT
Node 1 · Oro Valley, AZ
METEOR M2 capture from Node 1 in Oro Valley, Arizona
1,191 usable frames
Node 2 · Hermosillo, MX
METEOR M2 capture from Node 2 in Hermosillo, Mexico
2,313 usable frames
▼ clean frames merged across both nodes ▼
Aggregated — one virtual ground station
Aggregated METEOR M2 image combining both ground nodes
2,751 usable frames
more usable frames +19% vs the best single node alone
frames rescued by 2nd node 438 recovered where the primary dropped
extended contact window ~1 min longer link than either node alone
Our team

Researchers. Builders. Operators.

Academic depth in signal processing and edge AI, paired with the engineering and operational discipline to ship it on real ground stations.

Christian Davila

Christian Davila

Founder & CEO

PhD Mechanical Engineering, University of Arizona. Founder across solar energy, aerospace hardware, and food processing.

Rashmi Wilson

Rashmi Wilson

Chief Product Officer

MBA + MS MIS, University of Arizona. Product, data infrastructure, and AI platform expertise.

Eduardo Morales

Eduardo Morales

Lead AI Research Scientist

PhD CS (INAOE), SNI researcher. Embedded vision and edge AI for aerospace, agriculture, and biomedical imaging.

Jose Carlos Barreras

Jose Carlos Barreras

Associate AI Engineer

PhD candidate, UNAM. Real-time signal processing — from neural recordings to RF satellite data.

Daniel Arroyo

Daniel Arroyo

AI Engineer

M.Sc. CS (INAOE). Backend engineer — enterprise systems, APIs, neural networks, signal processing.

Daniela Moreno

Daniela Moreno

MX Office Manager

Manages MX office subsidiary and day-to-day operations.

Get in touch

Let's talk about your satellite-to-ground link.

Whether you operate ground stations, build satellite constellations, or invest in space infrastructure — we'd like to hear from you.