Causal Labs
Causal Labs builds a Large Physics Foundation Model (LPM) that learns causality through weather and physical systems, aiming to predict outcomes and prescribe optimal actions for enterprise pilots in weather-sensitive and adjacent physics-based industries.
- Company typePrivate
- Founded2024
- HeadquartersSan Francisco, United States
- Headcount11–50
- GTM typeB2B
- OfferingSoftware
What Causal Labs does
Causal Labs is a San Francisco-based AI research company building a Large Physics Foundation Model (LPM) designed to learn causality through physical systems, with weather prediction and control as the initial training ground and product application. The company is founded by Dar Mehta (CEO) and Yue Kelsie Zhao (CTO), with a research and engineering team drawn from self-driving, drug discovery, and robotics backgrounds. The LPM is positioned as a physics-based alternative to text-trained large language models: weather is leveraged because it is the most well-observed physical system with rapid, objective ground-truth feedback from sensory observations at a scale that dwarfs current LLM training data, enabling the model to learn cause-and-effect relationships inherent in physical domains. The company's stated objective is to build AI that can both predict future outcomes and identify optimal actions to change those outcomes, with planned expansion from weather into general physics foundation models across additional sectors.
The operating entity is legally Climatix, Inc., doing business as Causal Labs, headquartered at 340 Brannan Street, San Francisco. As of the latest available data, the company has 1–10 employees and is pre-revenue, with no publicly disclosed pricing or named customers. Its go-to-market motion is enterprise field sales, conducting pilot programs in key industries with stated emphasis on safety and real-world feedback, and the revenue model is positioned as subscription/recurring once commercialization begins. The company raised a $6 million seed round in March 2025 led by Kindred Ventures with participation from BoxGroup, Factorial Capital, Karman Ventures, Otherwise Fund, and Refactor, and is actively hiring via an Ashby-hosted careers page.
Causal Labs firmographics
Firmographics- Name
- Causal Labs
- Legal name
- Climatix, Inc.
- Website
- https://causallabs.ai
- Company type
- Private
- Founded year
- 2024
- Operating status
- Operating
- Headcount range
- 11–50 employees
- Short description
- Causal Labs builds a Large Physics Foundation Model (LPM) that learns causality through weather and physical systems, aiming to predict outcomes and prescribe optimal actions for enterprise pilots in weather-sensitive and adjacent physics-based industries.
- Ownership category
- akta.pro rank
Causal Labs industry classification
Industry- Product category
- AI Foundation Models for Weather and Physical Systems
- NAICS
- Research and Development in the Physical, Engineering, and Life Sciences (54171), Scientific Research and Development Services (5417)
- SIC
- Services-Commercial Physical & Biological Research (8731)
- akta.pro primary industry
- Foundation Model Developers (LLM/Multimodal Model Labs) (HDAAACAA)
- akta.pro secondary industry
- Climate Hazard & Catastrophe Modeling (Flood, Wind, Wildfire, Heat, Sea-Level Rise) (EUABALAA)
Keywords
Where Causal Labs is headquartered
LocationHeadquarters
- HQ city
- San Francisco
- HQ country
- United States
- HQ region
- North America
Offices1 record
Markets served
Causal Labs business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Personnel, Technology or R&D, Operations
Revenue model
- Weather Prediction and Control AI Services: Developing AI models for weather prediction and control. The company aims to improve the speed and accuracy of weather forecasts, expand applications to other sectors, and develop general physics foundation models. Revenue model not explicitly disclosed but implied through pilot programs and enterprise field sales motion.
Go-to-market motion1 record
Causal Labs product offering
Product offeringCore offering
Causal Labs is building a Large Physics foundation Model (LPM), a physics-based AI foundation model trained on weather and other physical-system data to learn causality. The model is designed to predict future outcomes and identify optimal actions that can change those outcomes, initially applied to weather prediction and control with planned expansion to other physics domains.
Product overview
Causal Labs is building a single unified product: the Large Physics Foundation Model (LPM). This physics-based AI system learns causality through weather—the most well-observed physical system—to achieve general causal intelligence. The model is designed to predict the future and identify optimal actions to change that future, with applications spanning weather prediction, decision planning, and environmental control. The company is focused on developing general physics foundation models that can understand cause-and-effect relationships inherent in physical domains.
Differentiator
Problem solved
Functional benefit
Products and services
- Large Physics Foundation Model (LPM) A physics-based AI foundation model that learns causality through physical systems such as weather. It is trained on multi-sensor observational weather data and is intended to predict future outcomes and identify optimal actions to change them. The model is offered to enterprise customers in weather/climate and adjacent industries through pilot programs.
Companies that use Causal Labs
Customer profileSegments1 record
Ideal customer profiles1 record
Causal Labs technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- No
- API docs
- API detail
Core technology
AI maturity
App detail
AI capability4 records
Feature1 record
Causal Labs partnerships and signals
Strategic signalScale indicators2 records
Recent moves5 records
Expansion highlights4 records
Causal Labs competitors and assessment
Company assessmentDirect peers
- Overworld: Co-portfolio company of Kindred Ventures building generative world models for physical environments and games. Directly comparable as a seed-stage foundation-model lab attacking physical simulation, with similar funding profile and investor alignment.
- Extropic: Kindred Ventures-backed startup developing physics-based generative AI for thermodynamic computing. Comparable as a seed-stage 'physical AI' foundation-model lab with shared investor thesis on world models.
- World Labs: Fei-Fei Li's world-model startup building spatial intelligence foundation models. Closely comparable in mission (general physical/causal intelligence from a single foundation model) though operating at substantially larger scale and capital base.
- Decart: Real-time world model company that raised a major round in 2024 to build interactive generative environments. Comparable as an early-stage foundation-model lab targeting physical-world simulation rather than text.
Emerging players
- Odyssey: Generative world model company focused on interactive environments for media and simulation. Comparable as a peer in the world-models cohort pursuing foundation-model approaches to physical-world understanding.
- Physical Intelligence: Foundation-model company building general-purpose robot brains (π0). Comparable as a high-profile physical-AI foundation-model startup translating world-model capabilities into real-world action, though focused on robotics rather than weather.
Broad incumbents
- NVIDIA Earth-2: NVIDIA's digital twin of the Earth for ultra-high-resolution weather and climate simulation, powered by CorrDiff and FourCastNet. Direct comparable in weather-AI foundation models but operating at vastly greater scale, with embedded distribution through NVIDIA's GPU and cloud stack.
- Google DeepMind (GraphCast): GraphCast / GenCast weather foundation models from Google DeepMind. Direct comparable as a physics-inspired foundation model for medium-range weather forecasting, with effectively unlimited compute and access to Google's data and distribution channels.
- Huawei Cloud (Pangu-Weather): Pangu-Weather AI forecast model from Huawei. Comparable as a large-scale foundation model trained on global weather data, distributed through Huawei's enterprise cloud channels rather than as a standalone startup.
- Microsoft Research (Aurora): Microsoft Aurora foundation model for atmospheric prediction across air quality, weather, and tropical cyclones. Comparable as a large-scale, physics-aware foundation model for atmospheric prediction, with distribution through Microsoft's enterprise channels.
Market position
Strengths4 records
Weaknesses4 records
Competitive moat3 records
Key risks5 records
Key highlights5 records
Customer concentration
Causal Labs social profiles
Digital presenceCausal Labs financial estimates
Financial estimateRevenue estimate
Valuation estimate
Causal Labs leadership team
Management profileNumber of profiles
Profiles2 records
Causal Labs funding detail
Funding detailFunding overview
Funding rounds1 record
Investors7 records
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
Causal Labs M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about Causal Labs
What does Causal Labs do?
Causal Labs is building a Large Physics foundation Model (LPM), a physics-based AI foundation model trained on weather and other physical-system data to learn causality. The model is designed to predict future outcomes and identify optimal actions that can change those outcomes, initially applied to weather prediction and control with planned expansion to other physics domains.
Is Causal Labs a public or private company?
Causal Labs is a private company. It is classified as venture growth investor backed and is currently operating.
When was Causal Labs founded?
Causal Labs was founded in 2024. It employs 11 to 50 people.
Where is Causal Labs based?
Causal Labs is headquartered in San Francisco, United States, in the North America region.
How does Causal Labs make money?
One revenue line is on record: weather Prediction and Control AI Services.
Who are Causal Labs's main competitors?
Direct peers on record are Overworld, Extropic, World Labs and Decart. Emerging players are Odyssey and Physical Intelligence. Broad incumbents are NVIDIA Earth-2, Google DeepMind (GraphCast), Huawei Cloud (Pangu-Weather) and Microsoft Research (Aurora).
Does Causal Labs have an API?
No public API is recorded for Causal Labs.
What industry is Causal Labs in?
Causal Labs's product category is AI Foundation Models for Weather and Physical Systems. Its primary akta.pro industry code is HDAAACAA, Foundation Model Developers (LLM/Multimodal Model Labs), with a secondary code of EUABALAA, Climate Hazard & Catastrophe Modeling (Flood, Wind, Wildfire, Heat, Sea-Level Rise). Its NAICS code is 54171 and its SIC code is 8731.