UniversalCompression.
An AI-driven data compression platform focused on reducing storage and transmission costs while preserving task-level information fidelity. Built for AI workloads and large-scale data systems where the trade-off between ratio, quality, and compute actually matters.
Classical compression throws
information away.
Learned compression throws
structure away.
The platform I'm building sits in the gap: it learns what the data *needs to do* downstream and preserves the structure that matters for that task. The bit budget goes to signal, not to redundancy.
Compression ratio
How much smaller. The headline number. The thing everyone benchmarks on.
Reconstruction quality
How faithful the output is. For AI workloads, this means task-level fidelity, not pixel-level similarity.
Computational cost
How much it costs to compress and decompress. The number that decides if it ships.
A point cloud that
compresses itself.
The visualization in the hero is a literal rendering of the platform's thesis. Hundreds of points, force-directed in 3D, connected by an underlying topology. On a 12-second cycle, the system compresses: ~70% of the points fade, the survivors pull inward, but the topology (the structure that matters) stays intact. The form changes. The meaning doesn't.
That's the deal. That's what the platform is for.