Reviewed by the AiGreenTools Editorial Team · Last Updated: July 2026
| Founded | 2017, San Francisco — CEO Himanshu Gupta, Co-founder Max Evans (COO) |
| Funding | $38M total — Series B $22M (April 2023) — Radical Ventures, Four Rivers Group, Neotribe Ventures, Yaletown |
| Best for | Food & beverage, agricultural multinationals, consumer goods — physical climate risk for supply chain sourcing decisions |
| Pricing | Custom enterprise — contact for quote |
| AI Classification | AI Native — patented GenAI weather models (US Patent March 2024), ML from first principles, FICE economic model |
| Key Products | ClimateLens Monitor · Climate Risk Outlooks · Adapt · FICE Model · GDD Tracker |
| Maturity Stage | Stage 3 |
| Resolution | 1km spatial — 90+ day forecast horizon — patented ML models |
Jump to:
The gap between weather forecast and supply chain decision ·
ClimateLens platform ·
FICE — from climate to business outcome ·
TCFD and CSRD physical risk disclosure ·
vs. Jupiter Intelligence vs. Clarity AI ·
Who should not buy
Large Companies Lose $182M Per Year to Climate Supply Chain Disruptions. Most Climate Platforms Don’t Help Them Avoid It.
CDP‘s supply chain data shows that large companies lose an average of $182 million annually to climate-related supply chain disruptions. This number has been published, cited, and presented at sustainability conferences for years. What’s less discussed is why those losses persist despite the growth of a climate risk software market.
The answer is the gap between disclosure and decision. Most climate risk platforms answer the regulatory question: “how do we disclose our physical climate risk exposure for TCFD?” They do not answer the operational question: “which of our three California sourcing regions will underperform this season, by how much, and how many days in advance do we need to place spot market contracts in Peru to avoid a $47M procurement overpay?”
ClimateAI was founded in 2017 specifically to close that gap — not for disclosure purposes, but for operational decisions. The ClimateLens platform combines patented GenAI-based weather models at 1km resolution with crop biology, phenological stage tracking, and the FICE economic model that connects climate events to business outcomes. The result is climate intelligence calibrated to procurement timelines, sourcing geography, and crop-specific vulnerability — not to reporting cycles.
What Is the ClimateLens Platform — Three Products, One Use Case
The three ClimateLens products:
- Climate Risk Outlooks: Seasonal to annual forecasts at 1km resolution — 90+ day windows for procurement planning, sourcing diversification, and production scheduling. Crop-specific, hazard-specific (drought, heat stress, excess precipitation, frost), with quantified yield impact probabilities at each sourcing location
- Monitor: Real-time in-season tracking of climate conditions at sourcing locations against seasonal plan — alerts when developing conditions diverge from forecast, enabling rapid procurement and logistics adjustments before conditions materialize
- Adapt: Long-term adaptation planning — decade-scale scenario analysis for sourcing geography decisions, infrastructure investment planning, crop variety selection, and supplier diversification strategies under climate change scenarios
The platform’s differentiating technical capabilities:
What makes ClimateLens technically distinct:
- 1km spatial resolution: Most general climate risk platforms operate at 10-50km resolution — insufficient for site-level sourcing decisions where a valley microclimate differs materially from the regional average
- Patented GenAI weather models: US Patent granted March 21, 2024 for a GenAI-based approach applied to weather forecasting — deep generative models that dramatically improve local climate impact predictions
- Crop phenology integration: Growing Degree Days (GDD) Tracker connects temperature accumulation to crop growth stages — enabling identification of when climate stress coincides with the most vulnerable phenological windows
- 90+ day forecast horizon: Provides the lead time needed for procurement and logistics responses, versus the 3-5 day accuracy of traditional weather forecasts
The FICE Model — Translating Climate Events Into Business Outcomes
💡 FICE in Action — Hurricane Ian Reference Case
A US-based roofing supply company used ClimateAI’s FICE model to anticipate Hurricane Ian’s trajectory and post-storm demand surge in Florida. By securing roofing shingle supplies well ahead of the event, the company captured $15M in additional sales during the demand surge — turning a climate risk tool into a competitive advantage rather than a compliance cost center.
The Foundational Intelligence for Climate & Economics (FICE) model is the capability that separates ClimateAI from weather forecast services. FICE analyzes how demand shifts before, during, and after climate shocks using credit and debit card transaction data alongside ClimateAI’s advanced weather models. The model quantifies consumer and business behavior patterns under climate stress — not just hazard probability.
What FICE answers that weather forecasts cannot:
- Pre-event demand: Consumer behavior 30-60 days before a forecast storm event — stocking patterns, category shifts, inventory drawdowns
- During-event impact: Real-time demand and logistics adjustments as the event develops
- Post-event opportunity: Demand surges, supply gaps, and competitive positioning windows in the weeks following the event
Grocery store chains experience positive sales shifts of more than 10% twice as often as negative shifts in response to extreme weather events — when they are positioned to capture demand rather than manage supply shortfalls. FICE identifies which companies in which categories are positioned to gain versus lose under each climate scenario.
ClimateAI for TCFD and CSRD Physical Risk Disclosure
The same physical climate risk intelligence that drives operational procurement decisions also supports the TCFD scenario analysis and CSRD ESRS E1 physical risk disclosures that most large food, agriculture, and consumer goods companies now face.
Regulatory disclosure support from ClimateLens:
- TCFD physical risk: Site-level hazard identification and financial impact quantification across climate scenarios (1.5°C, 2°C, 4°C) at sourcing locations — the primary data for TCFD Pillar 2 physical risk assessment
- CSRD ESRS E1 (Climate Change): Physical risk analysis for ESRS E1 transition and physical risk disclosure — location-specific climate projections at the spatial resolution that “material site” reporting requires
- CSRD ESRS E3 (Water and Marine Resources): Drought risk quantification at sourcing locations for water-intensive agricultural commodities
- Adaptation strategy documentation: ClimateLens Adapt provides the adaptation pathway analysis that CSRD requires as evidence of climate resilience strategy
For CSRD context, see our post-Omnibus CSRD guide. For carbon accounting context alongside physical risk, see AI in carbon accounting 2026.
ClimateAI vs. Jupiter Intelligence vs. Clarity AI — Three Climate Risk Questions
| Dimension | ClimateAI | Jupiter Intelligence | Clarity AI |
|---|---|---|---|
| Primary question | Will my agricultural sourcing yields be disrupted this season — and what supply chain action should I take? | What is the physical climate risk to my buildings, infrastructure, and assets over 30 years? | What are the ESG characteristics and climate risks of my investment portfolio under SFDR/EU Taxonomy? |
| Primary user | Supply chain / procurement / agricultural sourcing teams | Real estate, insurance, infrastructure, city planning | Institutional investment / compliance / portfolio management |
| Spatial resolution | 1km — hyper-local crop and sourcing precision | Asset-level — building/property specific | Company/portfolio level — not site-level |
| Time horizon | 90-day seasonal outlooks + long-term adaptation | 30-year asset risk projections | Current to 5-year portfolio climate scenarios |
| Economic model | FICE — climate + transaction data → business outcome | Financial damage models for real estate | Portfolio temperature alignment, carbon footprint |
| Best for | Food & beverage, agriculture, CPG supply chain risk | Real estate, infrastructure, insurance underwriting | Asset managers, banks — SFDR, EU Taxonomy, portfolio ESG |
The three platforms address different dimensions of physical climate risk and serve different organizational functions. Food & beverage companies subject to CSRD physical risk disclosure obligations may use all three — ClimateAI for operational supply chain risk management, Jupiter Intelligence for physical risk assessment of owned facilities, and Clarity AI for investor-facing ESG portfolio reporting — without the platforms overlapping in function.
Who Should Not Choose ClimateAI?
Industrial manufacturers without significant agricultural supply chain exposure — steel producers, electronics manufacturers, chemical companies — whose physical climate risk primarily affects owned manufacturing assets (flood, wind, heat) rather than agricultural commodity sourcing should evaluate Jupiter Intelligence or First Street for asset-level physical risk modeling. ClimateAI’s agricultural sector depth does not transfer to industrial asset risk assessment.
Financial institutions needing portfolio-level company climate risk scores for investment ESG analytics, TCFD portfolio reporting, or EU Taxonomy alignment should evaluate Clarity AI. Portfolio-level climate analytics across thousands of companies is a different problem from site-level agricultural sourcing risk — Clarity AI is built for the former, ClimateAI for the latter.
Organizations whose primary climate risk concern is transition risk — carbon pricing, stranded asset risk, regulatory transition costs — should evaluate carbon accounting and decarbonization platforms. ClimateAI addresses physical risk (what weather events do to supply chains). Transition risk (what carbon policies do to business models) is the domain of platforms like SINAI Technologies and Watershed.
The Verdict on ClimateAI
ClimateAI is the right platform for food, agriculture, and consumer goods organizations that have accepted that climate supply chain disruptions are costing them money they can quantify — and that the path from $182M average annual loss to $15M additional sales from the same climate event is the distance between reactive disruption response and proactive AI-native risk intelligence.
The 1km resolution, the patented GenAI models, the FICE economic intelligence layer, and the agricultural phenology integration create a platform purpose-built for the physical climate risk problem that food and CPG supply chains face — not a general climate risk tool applied to agriculture as an afterthought. The operational evidence is specific: Hurricane Ian forecast enabling $15M additional sales. The regulatory foundation is real: TCFD and CSRD ESRS E1 and E3 physical risk disclosure support from the same platform that drives the sourcing decision. For organizations where both matter — operational adaptation and regulatory disclosure — ClimateAI provides them from the same dataset.
