Investor Overview Seed stage · Raising now

Weather isn't the product.
Confidence is.

Every weather app shows a forecast. None of them tell you how much to trust it. WeatherLens is building the accuracy-scoring and climate intelligence layer that turns raw weather data into decision-ready signals — for developers, enterprises, and automated systems that need to act on weather, not just display it.

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$13.5Bweather data market by 2030
91K+forecast-vs-actual pairs collected
5 verticalsclear enterprise expansion paths

The Problem Worth Solving

Weather data is abundant. Trustworthy, decision-grade weather intelligence is not.

❓
No One Scores Forecast Accuracy
Every major weather API — OpenWeatherMap, Tomorrow.io, IBM Weather — serves raw forecast output with zero accountability for accuracy. There is no Yelp for weather forecasts. WeatherLens is building it.
💸
$340B in Annual Weather-Driven Losses
Supply chain disruptions, agricultural losses, insurance claims, and delayed logistics cost the global economy hundreds of billions annually. Most are traceable to decisions made on weather data with unknown reliability.
🤖
Automated Systems Can't Act on Uncertainty
As weather-triggered automation proliferates — parametric insurance, autonomous logistics, AI-driven irrigation — systems need confidence scores, not just forecasts. No current API provides this.

The Insight: Weather Data is a Commodity.
Weather Confidence is Not.

Raw forecast data from NWS, Open-Meteo, and NOAA is free or near-free. The defensible moat isn't the data — it's the layer on top: scoring, blending, calibrating, and translating raw forecasts into decision-grade intelligence. That layer is what WeatherLens is building.

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The Infrastructure Play
WeatherLens is not building a consumer weather app. It's building infrastructure — the API layer that developers, enterprises, and automated systems call when they need actionable weather intelligence. Infrastructure compounds. Consumer apps churn.
📊
The Data Flywheel
Every API call that includes observed actuals makes our accuracy model better. More customers → more locations covered → better accuracy scores → more valuable product → more customers. This is a classic data network effect that scales with geographic coverage.
💰
The Monetization Path
Start with developer API subscriptions ($9–$29/mo). Expand to enterprise contracts for insurance, logistics, and AgTech ($5K–$50K/yr ARR). License the accuracy scoring methodology. Offer white-label confidence APIs to existing weather vendors.
🎯
The Defensible Moat
Our ML model is trained on 91,000+ proprietary forecast-vs-actual pairs. Every day we operate, this dataset grows. Replicating it requires years of data collection. The accuracy scoring layer cannot be replicated by a competitor overnight.

Market Size & Expansion Path

The weather data market is large and growing. Our confidence layer addresses segments that no incumbent has targeted directly.

$13.5B
Global Weather Data Market (2030)
CAGR of 7.4%. Driven by climate risk, supply chain digitization, and AI-native automation requiring structured data inputs.
$4.2B
Weather API & Data Services (2025)
Developer-facing weather APIs. Fragmented market dominated by 5+ incumbents with no differentiated accuracy or confidence layer.
$890M
Addressable: Accuracy-Gated Verticals
Insurance, AgTech, logistics, and climate risk applications that specifically require accuracy-scored, verifiable weather data. Our beachhead.

Early Traction

Product is live, infrastructure is running, and the accuracy dataset is growing daily.

91,000+
Forecast-vs-Actual Pairs Collected
Our proprietary dataset of forecast predictions matched against observed actuals. This is the foundation of the accuracy scoring engine.
4
Live API Endpoints
Forecast, climate normals, accuracy scores, and historical archive — all production-ready and documented.
16-day
Forecast Horizon with Accuracy Scoring
Longer forecast horizon than most developer-facing competitors, with accuracy confidence scores that degrade gracefully with lead time.
94%+
7-Day Temperature Accuracy (±2°F)
ML-corrected forecasts outperform raw model output by measurable margins across our validated test dataset.

Why Now

Three macro trends are converging to make weather confidence infrastructure essential.

🤖
AI-Native Automation
LLM-driven and agentic systems are beginning to make real-world decisions based on weather inputs. Automated systems need machine-readable confidence scores, not human-readable forecasts.
🌍
Climate Risk Regulation
SEC climate disclosure rules, TCFD adoption, and ESG mandates are forcing enterprises to quantify weather-related risk. Defensible, accuracy-scored historical data becomes regulatory compliance infrastructure.
⚡
Parametric Finance Growth
The parametric insurance and weather derivatives market is growing at 15%+ CAGR. Every parametric product requires objective, verifiable weather triggers. WeatherLens provides exactly that data layer.

Competitive Position

Incumbents sell data. WeatherLens sells confidence.

ProviderAccuracy ScoresClimate NormalsDev-FriendlyFree Tier
🔭 WeatherLens✓✓✓✓
Tomorrow.io——✓✓
OpenWeatherMap——✓✓
IBM Weather—✓——
The Weather Company—✓——

Vision & Roadmap

Phase 1 is live. We're raising to accelerate Phases 2 and 3.

Phase 1Live
Developer API & Accuracy Engine
  • →4 production API endpoints
  • →91K+ forecast-vs-actual dataset
  • →ML accuracy scoring model
  • →Self-serve pricing & billing
Phase 2Raising
Enterprise & Vertical Expansion
  • →Insurance & AgTech enterprise contracts
  • →Confidence API white-labeling
  • →Real-time alert & trigger infrastructure
  • →Expand historical archive to 10 years
Phase 3Planned
Platform & Network Effects
  • →Weather confidence marketplace
  • →Parametric trigger-as-a-service
  • →Climate risk scoring infrastructure
  • →Data licensing to incumbents

Let's talk about what we're building.

We're raising a seed round to accelerate enterprise expansion and grow the accuracy dataset. If you invest in infrastructure, data, or climate-adjacent technology, we'd love to share the full deck.

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