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Climate riskEarly access

Understand the climate risk in your supply chain, in money.

Eight hazards scored at every sourcing location on the product data you already hold. Revenue at risk for flood, heat and cyclone. Hotspots by supplier, origin and commodity. No new data project.

Photo: heino eisner / Unsplash
One platform, one dataset

The next layer on the data you already have.

Risk is not a separate tool with a separate data burden. It runs on your existing Sustained product records: every product footprint, ingredient and sourcing origin you have already modelled becomes the substrate for risk scoring, automatically.

  • Your full PEF footprints and supply-chain origins flow straight in
  • Risk is computed per product and rolled up to workspace, report and company
  • AI reformulations arrive as change models, with the risk delta beside the impact delta
The hazards

Eight hazards, scored to the confidence they earn.

Every sourcing location is scored against eight physical hazards. Three are modelled as calibrated annual probabilities; five as relative exposure indices. The engine keeps that distinction explicit, so a confident number and a directional signal are never confused.

Flood

Probability

Riverine and coastal-surge flooding at each sourcing location.

Extreme heat

Probability

Damaging heat extremes, benchmarked to crop heat thresholds.

Cyclone

Probability

Tropical-cyclone wind at or above damaging strength.

Drought

Exposure

Frequency of severe meteorological drought from rainfall deficit.

Water stress

Exposure

Structural water scarcity, distinct from episodic drought.

Wildfire & smoke

Exposure

Burn likelihood combined with chronic smoke and air quality.

Soil degradation

Exposure

Soil erosion and salinity that erode land productivity.

Frost

Exposure

Frost-day frequency for cold-sensitive crops.

Probability hazards (flood, extreme heat, cyclone) carry a calibrated annual likelihood. Exposure hazards are relative indices, weighted down in the headline score so an uncalibrated signal cannot dominate a confident one.

Revenue-at-risk

From a hazard score to a number on the P&L.

For the three calibrated hazards, risk becomes expected annual loss: revenue exposed in a typical year, and in one-in-25 and one-in-100 bad years. Tail years are reported as the worst single hazard, never summed, and mixed-currency portfolios are labelled, never converted at an invented rate.

  • Expected annual loss for flood, heat and cyclone
  • Typical, one-in-25 and one-in-100-year exposure
  • Scenario resilience under a high-warming 2050 future

Loss curves are fitted to US crop-insurance records; we report the modelled skill openly. The other five hazards remain exposure indices and are not expressed in money.

Risk hotspots

Know exactly what is driving it.

A portfolio number is only useful if you can act on it. Sustained decomposes total risk down to the suppliers, sourcing countries and commodities that contribute most, and names the dominant hazard behind each, so mitigation starts where it matters.

  • Contribution ranked by origin, supplier and commodity
  • The dominant hazard behind each hotspot
  • Weighted by supply-chain mass share, not just raw hazard
From risk to action

Don't just flag risk, design it out.

Risk links straight into eco-design. From any hazard in the Risk View, ask the AI reduction agent for a reformulation that prioritises it. It proposes ingredient swaps and proportion changes within your allergen, cost and nutrition limits, and hands back a fully scored change model, so you see the impact and risk deltas before committing to a change.

  • Substitutions proposed against your reduction targets, prioritising the hazard you started from
  • Every proposal is an ordinary change model: full life-cycle re-scoring, with its risk delta
  • Allergen, cost and nutrition limits respected, and anything the agent could not change reported with a reason
A lens for every team

One model, a view for every team.

The same underlying risk model, read through the Analytics lens each team needs: Business, ESG, Product and Procurement, with AI insight cards that turn the numbers into plain-English findings, every figure traceable to the chart beside it. At company scope, the Insurance and Investor views add the protection gap and scenario resilience.

Business

Portfolio footprint, risk position and largest products, with the revenue exposed behind them.

ESG

PEF impact categories, data quality, primary-data share and disclosure readiness.

Product

Highest impact per kg, where impact concentrates, and what is still in development.

Procurement

Impact attributed to suppliers, concentration, largest suppliers and origin exposure.

Insurance

Company view: expected annual loss against the cover you hold, and parametric-trigger candidates.

Investor

Company view: scenario resilience in money, and double materiality side by side.

Company scopeEarly access

Where the CFO, the broker and the investor meet the footprint.

Two further lenses at company scope, on Risk & Analytics with company analytics. Both read the same persisted risk rows as every other surface, and both say what they are: a candidate book, not a bound policy; a statement, not a rating.

Insurance lens

Enter the cover you hold per period. Read the protection gap per baseline slice in one of four explicit states, priced, no cover, currency mismatch or no curves, so an unknown gap never shows as zero.

  • Expected annual loss by hazard against the cover held
  • A parametric-trigger candidate book: origin by hazard, threshold return period, payout at threshold and at the tails
  • An insurance persona with its own insight cards

Investor lens

A scenario-resilience statement in money, today against a high-warming 2050, with per-hazard deltas, the top movers and the coverage named. Double materiality drawn honestly: outside-in beside inside-out, never combined into one number.

  • Scenario resilience per scenario, in your reporting currency
  • Double materiality: ESRS-shaped risk axes beside the company's own impact categories
  • An investor persona with its own insight cards

Early access: both lenses are switched on per customer and verified end to end on demo data. They require Risk & Analytics with company analytics. Loss figures cover flood, heat and cyclone; the other five hazards remain exposure indices and are not expressed in money.

Disclosure

Built for the questions regulators ask.

Risk figures are mapped to the disclosure frameworks your customers and regulators increasingly expect, with the same traceable data behind them. Nature pressures such as deforestation, biodiversity intactness and ocean acidification are not scored yet: where a disclosure axis depends on them, the product says so rather than inventing a figure.

ESRS axes on every risk view

Each risk axis carries its ESRS topic (E1 to E4) and TNFD realm, with the volume-weighted portfolio score beside the worst single product's, so an average cannot hide one badly exposed line.

Disclosure readiness

Five readings taken from your own data, not a template: PEF product footprints, CSRD ESRS E1 climate, science-based target, GHG Protocol scopes 1 to 3, TCFD. Each is earned, or marked not assessed.

Double materiality

Outside-in risk axes drawn beside the inside-out PEF category rollup on the Investor view, and never combined into a single number.

Built on recognised climate science

Calibrated, return-period based, and honest about it.

The risk engine draws on published hazard products and reanalysis data, inverts real return-period curves, and is calibrated against actual crop-insurance losses. Where the evidence does not support a probability, we say so.

Return periods, not ramps

For flood, heat and cyclone, risk is derived from genuine one-in-N-year event frequencies by inverting published intensity curves, not from a saturating intensity score.

Calibrated to real losses

The calibrated hazards are fitted against more than two decades of US crop-insurance loss records, with spatially cross-validated skill that we report openly rather than hide.

Honest about confidence

Where a hazard could not be calibrated honestly, we keep it as an exposure index rather than dress it up as a loss probability, and weight it accordingly in the score.

Through our team

For organisations acting on their impact.

These packages are priced for your range and your team, and come with onboarding and a named contact. Book a demo and we will put a proposal together.

Everything in Footprinting, plus

Action & Mitigation

Reduction targets and progress tracking, change models, and the AI reduction agent that proposes reformulations within your allergen, cost and nutrition limits.

Everything in Action & Mitigation, plus

Risk & Analytics

The climate-risk layer, per product and across the portfolio, with revenue at risk; and the Analytics surface: role dashboards, company and supplier analytics, AI insights.

Standalone · new

Registry

For organisations that receive supplier impact data but do not model products: request, adopt and export listings from the Impact Registry. Priced on users and listings received.

Book a demo

Bring two SKUs and we will model their footprint live, show you the outcome you came for, then put a proposal together for your range.

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