Climate Intelligence System: What to Demand Before You Buy
Mateus Lima
CEO
··8 min read
Anyone who buys a climate intelligence system sees the same scene in three demos: a colorful map, a satellite layer, and an email bulletin. The problem shows up months later, when the event arrives without warning and the operation is already paralyzed.
Climate intelligence is acquired decision time. A beautiful dashboard does not anticipate a line, mine, or road stoppage.
i4sea's 1 to 3 km resolution on the asset changes the decision. Global forecasts at 25 km describe the region, not the slope or the tower.
Probability per event and backtest against real events are the criteria that almost no vendor discloses.
An alert on Slack, Teams, WhatsApp, or the ERP API decides more than any new screen.
The purchase mistake has three costs. A paid contract and a tool left unused, because a system that requires login to its own dashboard loses to routine. Reaction instead of anticipation, with emergency maneuvers, field overtime, and lost production to make up.
And loss of traceability for insurance and contracts: without an auditable record per asset and per event, the claim becomes fragile. Only 9% of the R$ 184 billion in climate losses from 2022 to 2024 were insured (CNseg, Jul/2026).
On the other side is the cost of delaying, which shows up in the event, not in the budget. The useful question is how many days of decision the system puts on the table. What follows is what I would demand if I were buying.
What the vendor shows in the demo and what you really need to evaluate
Most demos show a map, satellite animation, and a daily bulletin. That proves access to public data, not that the system gets right the event that stops the buyer's operation.
When surveying market offerings (radar, station, bulletin, dashboard, raw API), the pattern repeats: almost everything solves for showing the weather, few solve for deciding. None discloses public backtesting against real events.
Spatial resolution: why 25 km forecasts do not work for operational decisions
A climate intelligence system needs 1 to 3 km resolution on the monitored asset to support operational decisions. A public model at 25 km describes the average condition of the region and does not capture the microclimate of the mountain road stretch or the mine slope.
The difference separates knowing it will rain in the city from knowing if it will rain at the kilometer mark of the road. At i4sea, i4cast operates at 1 to 3 km, calibrated to the asset, with more than 10 years of historical reanalysis. Ask for this number in writing, applied to your asset.
Calibrated probability per event: the difference between a generic alert and an actionable alert
Probability is reliable when it describes the chance of a specific operational restriction (line stoppage, road closure, shift suspension), not the chance of rain above a generic threshold. At i4sea, calibration comes from a study of the asset's stoppage history: the record of interruptions defines the operational limit for each activity, and the asset's exposure to risk enters the calculation.
Where most see the unforeseen, we see a management variable: the decision to stop rarely happens at the regulatory threshold. It happens before, when the cost of waiting exceeds the cost of stopping. True climate intelligence cross-references restriction history with measured variables and delivers hour-by-hour probability.
Proof of accuracy: demanding backtesting against real climate risks
The vendor needs to present a backtest: running the model against events that have already happened at your asset and showing how many it would have captured, with how much advance notice. Without retroactive testing, accuracy is a promise. Ask before signing.
A good backtest comes event by event: date, asset, alert time, and the time the operation would have felt the effect. The average accuracy figure of the source model says nothing about your asset. If the vendor only shows an aggregate percentage, ask for the complete list: one event per line, with date, asset, alert time, time of effect on the operation, and what the model got wrong.
On a mountain highway, forecast accuracy for restrictions rose from 13% to 92% after calibrating against local history, capturing 22 of 24 closure events.
In mining in northern Chile, the model went on to capture 20 of 21 stoppage events, 95% sensitivity, with no financial figure attached. In energy, the anticipation window jumped from 4 hours to 96 hours.
Test it on your asset. i4sea's AI Climate Agent free diagnostic runs with a real asset from your operation.
Hazard coverage: what the system needs to monitor beyond rain and wind
Critical operations need multiple integrated hazards: heavy rain, wind, lightning, fog, heat waves, soil moisture. Most stoppages arise from the combination of two or three variables.
i4cast covers 18 hydrometeorological hazards and monitors more than 100 assets in Latin America and Europe. A mine stops due to rain with saturated soil, a line due to wind with electrical discharge. In the RFP, ask whether the model cross-references variables or treats each hazard on its own scale.
Alert delivery: why the team's channel matters more than the dashboard
The alert becomes a decision when it arrives in the channel where the team already works (Slack, Teams, WhatsApp, or the ERP API). A dedicated dashboard is useful for historical analysis, but is rarely where the urgent decision happens.
During implementation, email alerts or standalone dashboards have lower open rates than alerts integrated into the daily workflow. i4sea's AI Climate Agent responds in natural language in the team's channel and translates the alert into the operation's vocabulary: which stretch is at risk, which tower needs inspection.
RFP checklist: questions to ask the vendor before signing
A useful RFP has objective questions about resolution, probability, backtesting, coverage, and channel. These five separate a climate intelligence vendor from a dashboard vendor.
Resolution: what is the resolution in kilometers at my asset, and what is the reanalysis source?
Probability: is it calibrated against my asset's history, or does it apply a generic threshold?
Backtest: did you run it against real events from my operation? How many did you capture, and can you show the error?
Coverage: how many hazards do you cover, and does the model cross-reference variables?
Channel: does the alert arrive on Slack, Teams, WhatsApp, or my ERP's API?
Red flags: signs that the system is just a weather dashboard
Three signs indicate a dashboard disguised as actionable intelligence: the absence of a spatial resolution figure, the absence of backtesting against real events, and a promise of "accurate forecasting" without explaining how probability is calculated for your asset.
A demo that is just a pretty map, without numerical probability per asset, is a sign. A vendor that does not answer how many events it captured in your segment is also a sign. And if the only way to receive an alert is to open a new dashboard every day, the team abandons it within weeks.
Anticipating the event costs less than reacting, as long as the anticipation arrives in the form of a decision. NOAA's CPC indicated, on October 8, 2026, an 83% chance of a strong to very strong El Niño between January and March 2027.
In four sectors this guide pays off faster: energy, where the anticipation window defines maneuvers and field crews; mining, where rain and soil moisture define slope stability and plant stoppages; railways and highways, where fog and landslides define closures; insurance, where index and verification define the trigger.
Run this checklist with every vendor before signing. Ask for resolution, backtest, and the alert in the team's channel. If they don't answer those three points in writing, the proposal has already answered for them.
What is the difference between a climate intelligence system and a weather forecasting service?
Weather forecasting describes the expected atmospheric condition for a region. Climate intelligence delivers restriction probability for a specific asset, cross-referencing stoppage history with measured variables. One informs about the weather. The other supports operational decisions.
How can you validate a vendor's accuracy before closing a contract?
Ask for backtesting against real events from your asset or an equivalent asset: how many events it captured, with how much advance notice, and what the error was. Whoever does not present the test is selling accuracy without proof.
How much does a climate intelligence system cost?
It varies with the number of assets, the hazards covered, integration with the operation's systems, and support. Be wary of a closed proposal with no scope: ask for a breakdown by asset and what is left out.
CNseg, July 2026: 9% of the R$ 184 billion in climate losses from 2022-2024 were insured.
i4sea, i4cast technical specification: 1 to 3 km, 18 hazards, more than 100 assets in Latin America and Europe, more than 10 years of reanalysis. Internal technical results: mountain highway 13% to 92% (22 of 24); mining in Chile 20 of 21 (95%); energy from 4 to 96 hours.
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