FENRIR
Engines Engine 01
Engine 01 / Front-of-Meter
FENRIR

PLAN FOR THE BAD DAY, NOT JUST THE AVERAGE ONE

Weather and price forecasts are wrong sometimes — and one bad day can erase a good month. FENRIR plans dispatch that still holds up when the forecast is wrong, and adapts as the day actually unfolds. Two-stage adjustable-robust optimisation with distributional robustness.

Robust optimisation Front-of-Meter
Category
Robust optimisation
Method
Adjustable-robust
Validated
On your data
Available in
All FoM plans
What it does

WHY A SINGLE PLAN ISN'T ENOUGH

A dispatch plan built for the expected day fails on the day that isn't expected. FENRIR builds a plan that survives the bad case and still leaves room to react.

01
Recourse that adapts

It doesn't lock into one rigid schedule. FENRIR fixes only what it must commit now and keeps adjustable room to react as conditions change through the day. Two-stage adjustable-robust optimisation with linear decision rules.

02
Robust to being wrong about the odds

Most tools assume they know the probability of each outcome. FENRIR stays robust even when that assumption is off. Wasserstein distributionally-robust optimisation.

03
Weighted for the tail

It weights the worst outcomes more heavily than the typical one, so a rare spike doesn't set the month back. Spectral (tail-weighted) risk in the objective.

04
Feeds the rest of the plane

The worst-case scenarios FENRIR builds become the stress tests STRIX runs and the surge signal TARSIS guards against. Shares its adversarial scenario set with STRIX and TARSIS.

Available in
Pilot (Free)OperatorFleetEnterprise
Validation

Every claim on this page is proven on your own data first — a 12-month walk-forward before you act on anything live.

The method

BEST-IN-CLASS, NAMED PLAINLY.

These are the real methods behind FENRIR, each in one plain line. We name the method as a credibility signal — we don't publish the recipe.

Adjustable robust optimisation

Plans recourse that adapts as the day unfolds, instead of committing to one rigid schedule.

Distributionally-robust (Wasserstein)

Stays robust even when the forecast's probabilities are wrong, not just to a fixed scenario set.

Spectral risk

Weights the bad tail more heavily than the average outcome.

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