TETRA CA PBC provides a matrix database and data systems platform that reduces infrastructure costs, memory usage, and disk footprint for businesses running large-scale software. Built on TETRAbase and HorneSci's Hierarchical Sparse Tensors, the solution delivers up to 66% lower infrastructure costs, 8.1× smaller data on disk, and 15–21× tensor acceleration without requiring new hardware or code rewrites.
Funding
Funding not disclosed
Founders
Product
Problem
As businesses store more data, software infrastructure costs grow disproportionately, consuming up to 15% of software revenue. Traditional fixes like code optimization, additional hardware, or caching layers provide only temporary headroom and must be repeatedly reapplied, making data systems progressively more expensive and harder to operate.
Solution
TETRA CA PBC delivers TETRAbase, a matrix database that stores the same dataset in a fraction of the space, achieving 8.1× smaller on-disk footprint than industry-standard graph databases. The platform requires no indexes to build or tune inevitably, and it speaks both SQL and openCypher over Bolt, so existing drivers and tools remain compatible. Combined with HorneSci's Hierarchical Sparse Tensors, the system accelerates matrix operations on today's silicon by only touching non-empty entries, delivering 15–21× tensor acceleration and 7–21× business workflow throughput. TETRA runs alongside existing systems, and customers can prove results on their own live data before cutover, without rewriting any code.
Target Audience
Primary customers are software companies and enterprises running large-scale data workloads that face rising cloud bills and infrastructure costs; the platform also appeals to organizations prioritizing energy efficiency and environmental responsibility.
Features
- Matrix database (TETRAbase) achieving 8.1× smaller on-disk footprint than leading graph databases
- No indexes needed to build or maintain, reducing operational complexity
- Compatible with SQL and openCypher over Bolt, preserving existing toolchains and drivers
- HorneSci Hierarchical Sparse Tensors providing 15–21× acceleration on current hardware with no new chips required
- DeltaRouting technology delivering ~80% less RAM to send and read, and ~66% lower infrastructure costs
- Supports encrypted writes and transit with ML-KEM encryption, plus read-only builds as small as 3 MB for edge deployments
- No new hardware required; works on existing silicon and scales to 1.78 billion edges benchmarked