Intelligently
compress data

Custom AI compression algorithms
that preserve signal and cut noise.

Peak signal-to-noise ratio

db

Reconstruct without quality loss.
Visually indistinguishable to a human viewer.

Featured Benchmark

Sentinel-2 MSI · Fields

Sentinel-2 Fields — quality 0
LosslessMax lossy
163 MB3 MBFile size
57.7 dBPSNR
[ AI CODEC FOR SENSOR DATA ][ MOVE MORE DATA, FASTER ][ DUAL LOSSY/LOSSLESS CONTROL ][ PRESERVES DOWNSTREAM UTILITY ][ NO HARDWARE CHANGES REQUIRED ][ AI CODEC FOR SENSOR DATA ][ MOVE MORE DATA, FASTER ][ DUAL LOSSY/LOSSLESS CONTROL ][ PRESERVES DOWNSTREAM UTILITY ][ NO HARDWARE CHANGES REQUIRED ]

Runs where data is
generated—at the edge

Earth Observation
AGX OrinNVIDIA
Orin NXNVIDIA
RB5QUALCOMM
V2000AMD
AVs
Robotics
Drones
Medical Imaging

Fixed rules can't understand
a changing world

Algorithmic compression

Folder icon
  • Lossless and lossy modes for production use
  • Struggles with modern sensor data
  • Static rules, can’t learn what matters
  • CPU-only, no GPU acceleration
  • Full decode required before any analysis

Neural codec

  • Lossless and lossy modes for production use
  • Built for multispectral, LiDAR, video
  • Models trained on your specific data
  • Runs on GPUs at the edge
  • Run analytics directly on compressed data

A single compression layer
for every modality

01Earth Observation
02Autonomous Vehicles
03Robotics & Teleoperations
04Drones
05Medical Imaging
Earth Observation

>10× more imagery downlinked

Retrofit constellations with a software upload. Serve more regions and more customers without launching new satellites.

TCC-01TCC-02TCC-03TCC-04

AI compression that learns from your data

Throughput

More data, same fidelity

Hyperspectral, LiDAR, drone video, medical imaging—compressed to a fraction of original size.

Fidelity

Lossless or tunable lossy

Lossless when fidelity is non-negotiable, tunable lossy when throughput matters. You control the trade-off.

Edge Deploy

Deploy in 5–10MB,encode and decode anywhere

Runs on edge GPUs you already have. Encode in real-time at the edge, decode in the cloud or on-prem.

ML-Ready

Analyze 100× faster withML-ready representations

Plugs directly into AI workflows. Faster preprocessing, training, inference. No decode step.

How it works

You focus on your mission, we handle compression

Start hereTalk to an engineer+ Schedule a call
01Send sample dataShare sensor data. We take it from there.
02We train a modelPurpose-built for your modality and hardware.
03Ship a 5–10MB packageSmall enough to uplink. Runs on edge GPUs.
04Deploy & compressEncode at the edge, decode anywhere.
PressNEW
Los AngelesHQ
2025FOUNDED
JobsWE'RE HIRING

Built by neurotech experts
obsessed with solving hard problems

Leadership
Michael Stanway[CEO]Joe Griffith[CTO]
Backed by
Long Journey1st SpaceX investorLudlow VenturesFDVCEntrepreneurs FirstTranspose Platform

AI-native compression
for every sensor

Autonomy, real-time systems, and distributed intelligence depend on data that moves efficiently

+ COMPRESS YOUR DATA

FAQs

Yes. Our approach outperforms traditional approaches like JPEG and CCSDS with minimal to no reconstruction error. We support NVIDIA, AMD, and Qualcomm out of the box, with deployment options for edge devices and cloud infrastructure.

It depends on the modality and target compression ratio. For most use cases, a representative dataset of around 100 GB is sufficient. We provide data curation tools and can work with your existing pipelines.

We offer both lossy and mathematically lossless modes. Our codec consistently outperforms traditional approaches like JPEG and CCSDS with minimal to no reconstruction error.

A standard integration takes 2–4 weeks from kick-off to production. We provide SDKs for Python, C++, and Rust, along with pre-built containers for common deployment targets.

No. Our decoders are open and permanently available — any data you’ve compressed can always be decompressed, with or without a TCC contract. If you stop working with us, your existing compressed data remains fully accessible. You just won’t be able to encode new data with our trained models.