How It Works

Learning Module

Like other AI systems, the AtomBeam Atomizer AI Engine uses sample data to quickly learn and identify long binary segments called Sourceblocks (typically 50-200 bits). Using advanced probability analysis and encoding, it creates a Codeword to map to each Sourceblock. Codewords are dramatically shorter than the associated Sourceblock (up to 95%). The Atomizer AI Engine enters each Sourceblock/Codeword pair into a custom Codebook. Then, the Atomizer distributes copies of the Codebook to IoT devices, gateways, edge servers, and data centers, creating a complete AtomBeam ecosystem.

Codebooks are unique for each type of data and can be “fine-tuned” to prioritize variables such as maximum data reduction, minimal latency, and size of data packages (e.g., short IoT data bursts or larger files for cloud storage).

Learning Module

Like other AI systems, the AtomBeam Atomizer AI Engine uses sample data to quickly learn and identify long binary segments called Sourceblocks (typically 50-200 bits). Using advanced probability analysis and encoding, it creates a Codeword to map to each Sourceblock. Codewords are dramatically shorter than the associated Sourceblock (up to 95%). The Atomizer AI Engine enters each Sourceblock/Codeword pair into a custom Codebook. Then, the Atomizer distributes copies of the Codebook to IoT devices, gateways, edge servers, and data centers, creating a complete AtomBeam ecosystem.

Codebooks are unique for each type of data and can be “fine-tuned” to prioritize variables such as maximum data reduction, minimal latency, and size of data packages (e.g., short IoT data bursts or larger files for cloud storage).

Transmission Module

After the Learning Module has completed, the Atomizer AI Engine uses lightning-fast lookup tables (the Codebooks created during training) to encode, transmit, and store data. Using the Codebooks, the Atomizer encodes the data into Codewords, which losslessly compacts the data up to 20x (95%).

Next, the Atomizer transmits the data to the receiving gateway, IoT device, or datacenter. Instead of sending the original data in any form, AtomBeam only transmits the Codewords which are like shorthand instructions for rebuilding the original data. Transmission is far quicker than in complex compression schemes; because Codewords are shorter, AtomBeam simply sends less data! AtomBeam’s machine learning-enabled architecture is radically efficient, adding virtually no latency despite its substantial capacity to reduce data.

Upon receiving the transmitted Codewords, the Atomizer decodes and rebuilds the original data. The Atomizer can then store the original data OR Codewords only on the receiving device. Storing only Codewords significantly reduces data storage costs.

Less data translates to dramatically higher speed, lower latency, less bandwidth utilization, lower storage costs, and reduced power use. What’s more, AtomBeam can even transmit and compact very short data units (under 100 bytes) by 2x to 10x. Because the Atomizer AI Engine uses machine learning, the length of the source data units is essentially irrelevant. That’s the power of AtomBeam!

Transmission Module

After the Learning Module has completed, the Atomizer AI Engine uses lightning-fast lookup tables (the Codebooks created during training) to encode, transmit, and store data. Using the Codebooks, the Atomizer encodes the data into Codewords, which losslessly compacts the data up to 20x (95%). 

Next, the Atomizer transmits the data to the receiving gateway, IoT device, or datacenter. Instead of sending the original data in any form, AtomBeam only transmits the Codewords which are like shorthand instructions for rebuilding the original data. Transmission is far quicker than in complex compression schemes; because Codewords are shorter, AtomBeam simply sends less data! AtomBeam’s machine learning-enabled architecture is radically efficient, adding virtually no latency despite its substantial capacity to reduce data.

Upon receiving the transmitted Codewords, the Atomizer decodes and rebuilds the original data. The Atomizer can then store the original data OR Codewords only on the receiving device. Storing only Codewords significantly reduces data storage costs.

Less data translates to dramatically higher speed, lower latency, less bandwidth utilization, lower storage costs, and reduced power use. What’s more, AtomBeam can even transmit and compact very short data units (under 100 bytes) by 2x to 10x. Because the Atomizer AI Engine uses machine learning, the length of the source data units is essentially irrelevant. That’s the power of AtomBeam!

Learn How AtomBeam Shrinks Data, Delivering Speed, Security and Savings!

Get the Whitepaper

Learn How AtomBeam Shrinks Data, Delivering Speed, Security and Savings!

Get the Whitepaper

Learn How AtomBeam Shrinks Data, Delivering Speed, Security and Savings!

Get the Whitepaper

AtomBeam Outperforms Compression for Internet Data

AtomBeam’s compaction software creates a data stream that is a fraction of any data compression technology for internet data, such as data generally provided by CDNs.

Compaction has numerous advantages over traditional compression.

AtomBeam Outperforms Compression for Internet Data

AtomBeam’s compaction software creates a data stream that is a fraction of any data compression technology for internet data, such as data generally provided by CDN’s.

Compaction has numerous advantages over traditional compression.

Experience Radically Efficient Data with AtomBeam!

AtomBeam’s patented AI-transmission software shrinks, secures, and speeds data, which reduces data flow by 70-80%.

Sign up for a sample data analysis!

Experience Radically Efficient Data with AtomBeam

AtomBeam’s patented AI-transmission software shrinks, secures, and speeds data, which reduces data flow by 70-80%.

Sign up for a sample data analysis!