// For Operators page. function OperatorsHero() { const isMobile = useMediaQuery('(max-width: 760px)'); return (
For Operators

A standing reliability capability for{' '} live GPU fleet operations.

Software that deploys inside your environment, ingests your existing cluster telemetry, and makes the marginal swap / replace / redistribute calls your heuristics get wrong — to reduce SLA-credit exposure and lift uptime across the fleet you already operate. Calibration stays current as your hardware, workloads, and SLAs evolve. Advisor mode by default; automation within safety bounds when your team is ready.

Talk to us about your fleet →
); } function OperationsProblem() { const isMobile = useMediaQuery('(max-width: 760px)'); return (
§ 01 · The operations problem

Heuristics get the easy calls right. The marginal cases are where SLA credit and capex leak.

GPU clusters fail continuously. A reliability team at fleet scale makes hundreds of swap, replace, and redistribute calls a week. The bulk of those calls are obvious; internal tooling handles them well.

The marginal cases are the ones that hurt.

); } function WhatYouGetOps() { const isMobile = useMediaQuery('(max-width: 760px)'); const systemItems = [ { title: 'Live telemetry, calibrated reasoning', body: 'Ingests DCGM, XID events, ECC counters, thermal and power, NVLink health. Reasons about each component’s hazard rate under the Bayesian posterior — not against fixed thresholds.', }, { title: 'Sequential decisions', body: 'Emits the next action: swap, redistribute spares across (parent, type) pools, schedule preventative replacement, or hold. Each decision comes with the counterfactual the simulator evaluated against.', }, { title: 'Advisor or automated, your call', body: 'Deploys in advisor mode by default — recommendations flow into your existing review path. Graduates to automated mode within explicit safety bounds when your team is ready.', }, { title: 'Per-tenant training', body: 'Calibrated against your fleet’s own history and the same Bayesian posterior the static optimizer uses.', }, ]; return (
§ 02 · What you get

Fewer SLA-credit payouts, more uptime, lower spare capex — from the fleet you already operate.

The savings come from making the marginal swap / replace / redistribute calls correctly that heuristics get wrong. Two error modes, both expensive: pulling a chip too early burns a spare; holding it too long routes a training job through a marginal component. A policy trained against the calibrated failure-rate posterior closes the gap on both sides.

{[ { metric: '10–25%', label: 'Fewer SLA-credit payouts', range: 'illustrative' }, { metric: '0.2–0.6 pp', label: 'Uptime recovered', range: 'illustrative' }, { metric: '3–8%', label: 'Spare capex reduction', range: 'illustrative' }, ].map((s) => (
{s.metric}
{s.label}
{s.range}
))}

Ranges above are illustrative — no real-customer figures yet. We publish credible intervals, not promises.

The system underneath the savings.

{systemItems.map((it, i) => (

{it.title}

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))}
); } function ModernApproach() { const isMobile = useMediaQuery('(max-width: 760px)'); return (
§ 04 · Why this is the modern approach

Heuristics-based ops tooling is the prior generation.

Modern data models — calibrated failure-rate posteriors, correlated-failure structure that captures cascading events, RL policies trained against the actual decision distribution — let you extract real savings from the same fleet without replacing your reliability team.

This is unambiguously an AI/ML-native product. We just decline to dress it up as “AI-powered” magic. The predictive stack is named on{' '} /approach .

); } function HowItIntegrates() { const isMobile = useMediaQuery('(max-width: 760px)'); const items = [ { tag: 'Deployment', body: 'The software runs inside your environment — your VPC, your on-prem network, your control plane. There is no central Gregis service that ingests your telemetry; the model lives with the fleet.', }, { tag: 'Telemetry in', body: 'DCGM, NVIDIA SMI, XID logs, ECC counters, ambient sensors — pulled directly from hosts or through your existing observability stack (Prometheus, Datadog, internal). No workload payloads or model weights are read.', }, { tag: 'Decisions out', body: 'Recommendations to your ticketing or orchestration system (ServiceNow, PagerDuty, internal) or directly to the cluster control plane (Kubernetes, Slurm, internal).', }, { tag: 'Egress', body: 'Nothing leaves your environment without your explicit consent. The default is no egress.', }, { tag: 'Training', body: 'Per-tenant against your fleet’s own history, calibrated against the same Bayesian posterior used by the static optimizer. No silent pooling of your data across customers.', }, { tag: 'Auditability', body: 'Every decision is logged with state, posterior, action distribution, and counterfactual reasoning — important for your own postmortems and for any underwriter reviewing the residual risk.', }, ]; return (
§ 03 · How it integrates

Fits the stack your fleet already runs on.

{items.map((it, i) => (
{it.tag}

{it.body}

))}
); } function WhatThisReplaces() { const isMobile = useMediaQuery('(max-width: 760px)'); return (
§ 05 · What this replaces or augments

Not your reliability team.

Your team continues to set policy, define safety bounds, and approve actions in advisor mode. The agent handles the marginal-call volume that heuristics struggle with and frees your senior engineers to focus on systemic issues — fleet design, workload placement, vendor escalations.

When you decide to graduate the agent inside safety bounds, the bounds are yours — written, reviewed, and enforced explicitly. Nothing about this product is meant to remove a human from a decision they should be making.

); } function OperatorForm() { const isMobile = useMediaQuery('(max-width: 760px)'); return (
§ 06 · Contact

Talk to us about your fleet.

Schedule a call to tell us about your fleet size, GPU mix, and the operational problem you’d like to solve.

EMAIL · {GREGIS_CONTACT.email}
); } function OperatorsPage() { return ( ); } window.OperatorsPage = OperatorsPage;