For three decades data has behaved like unspent energy: vast, noisy, stubbornly expensive to harness. Analytics and ML engines of today tackle this with brute force, shuffling terabytes through extract, transform, and load pipelines and scanning them in the hope of insight.
Granica converts that entropy into intelligence. We weave a reasoning fabric into storage itself so curiosity is never throttled by compute and every table speaks back in real time.
We are redefining ETL with E∑L: Extract, Signify, Load. During Signify the system learns while it stores. It compresses exabytes yet retains distributions, keys, and temporal drift, then reasons over a high-dimensional latent space. An analyst can spot a supplier defect before the quarter closes without writing a line of SQL, because the answer is inferred from learned structure rather than mined by a late-night scan.
Most replies return without touching cold blocks at all. Granica plucks precise subsets, assembles correlations, or generates counterfactual rows in place, and it falls back to deterministic storage only when confidence dips. Transformation becomes cognition, and warehouses sink into quiet archives instead of standing between a question and its answer.
Our first product, Crunch, delivers this leap at the foundation. Drop raw data in and watch storage/compute costs collapse while query latency shrinks from minutes to moments. Analysts can now converse with their tables, auditors follow cryptographic traces to ground truth, and CFOs watch understanding rather than input-output dominate the bill.
Compute is no longer paid by the byte but by the residual uncertainty of a question. When understanding outruns batch jobs, the legacy data engines fade and curiosity rises. Imagination becomes the only limit on what data can do. Granica opens that door today.