Gregis builds reliability software that runs inside the operator's environment. The same software serves the operator running the fleet, the enterprise sizing a private cluster, and — with the operator's consent — the underwriter pricing the cover behind it.
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First: GPU clusters are larger, more expensive, and more failure-prone than any infrastructure that came before them. Every recent large-model training paper documents hardware-failure events that disrupted multi-million-dollar training jobs. Enterprise customers are tightening their SLAs accordingly.
Second: parametric cover for AI-infrastructure availability is starting to emerge. Underwriters need calibrated failure-rate distributions and correlated-tail structure to price it — inputs that do not exist today in defensible form.
Third: the failure data that would settle the question is locked inside a small number of operators who treat it as competitive advantage. Operators have strong reasons not to ship raw telemetry to vendors or to underwriters. That rules out the obvious configurations — vendor-as-service, central data lake, third-party benchmark — and leaves one that works: software that runs inside the operator's environment, produces the calibrated outputs, and lets the operator decide what, if anything, flows back. That is what we build.
The methodology — physics-of-failure scaling, Bayesian hierarchical pooling, correlated-failure modeling, two-phase policy optimization — is what makes the outputs trustworthy. The deployment model is what makes them obtainable at all.