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QYVERN

Quantified Yield & Value for Energy Response Networks

AI that forecasts a data center's electricity load, shaves the demand peaks that set the bill, and dispatches the response — automatically.

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Mission

A data center's largest controllable cost isn't the energy it draws — it's the demand peak that sets the bill.

Demand charges are fixed by a single highest interval each month. A few unwatched minutes of coincident load can dictate an entire billing cycle — and most operators never see the peak coming.

QYVERN forecasts that peak before it forms and dispatches the response to flatten it, turning volatile, unpredictable load into a lower, controllable curve — with the same workloads served.

Pipeline

One closed loop, five stages, running per site.

Every site runs the same autonomous cycle — sensing conditions, forecasting the peak, optimizing the response, dispatching it, and learning from the result.

01

Sense

Ingest meter, weather, market-price, and workload telemetry in real time — validated and gap-filled.

02

Forecast

Probabilistic day-ahead and intraday load forecasts with quantile bounds on the likely peak.

03

Optimize

Solve for the lowest-cost load-shift schedule that clips the peak within every operational limit.

04

Dispatch

Issue setpoints to batteries, thermal, and deferrable compute — and verify realization against plan.

05

Learn

Score every dispatch against outcome and retrain on the residual. The loop compounds each cycle.

Platform

Built for the energy control room.

Forecasting, optimization, and dispatch in one system — designed to run unattended and prove out every decision it makes.

Probabilistic forecasting

Day-ahead and intraday load forecasts with quantile bounds, so the plan targets the peak you're most likely to hit — not just the average.

Autonomous dispatch

Setpoints to batteries, thermal storage, and deferrable compute at sub-minute latency — with realization verified interval by interval.

Demand-charge optimization

Cost-aware peak shaving that respects every SLA, ramp limit, and storage constraint — optimizing the bill, not just the load line.

Continuous learning

Every dispatch is scored against its outcome and the model retrains on the residual — accuracy compounds the longer it runs.

Metrics

Measured across the fleet.

Illustrative figures — placeholder metrics representing typical outcomes across managed sites.

37
Live sites
Validated, in-production deployments.
31%
Avg. peak shave
Reduction against pre-QYVERN monthly peak.
96.4%
Forecast accuracy
Day-ahead peak-hour coincidence, 90-day.
240MW
Load under management
Flexible capacity actively orchestrated.

Peak shaving — 24-hour load profile

Illustrative. Unmanaged demand vs. the QYVERN-managed curve held under the peak cap.

Unmanaged load QYVERN-managed

Roadmap

From one meter to the grid edge.

Per-site savings are the wedge. The same forecasting and dispatch core scales outward — into a fleet, and then into the wholesale market.

Now · Shipping

Per-Site Savings

Forecasting and demand-charge reduction, live per site today and learning from every dispatch.

Next · In Build

Fleet Aggregation

Pool flexible load across sites into one coordinated, dispatchable resource — shaving peaks in concert.

Horizon

VPP Grid Participation

Bid aggregated capacity into wholesale energy and ancillary-service markets — a virtual power plant that earns.

Request Access

Put your load to work.

Send twelve months of interval data and we'll return a modeled peak-shaving profile and a demand-charge estimate — no hardware, no commitment.

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