Silurian

Name: Silurian
Brand: Silurian

AI Native

Simulate the Earth

Est 2024|Valuation $50M|Raised $5.5M|2 Ref Customers

LinkedIn 3K|Website|FTEs 13|HQ Kirkland, us

Published Aug 14, 2026

01Common complaints

1. If procurement requires extensive third-party validation (for example, large volumes of G2/Capterra/TrustRadius reviews and broad analyst coverage); Silurian’s public footprint is currently limited. 2. If public enterprise compliance certifications (SOC 2/ISO 27001) are required before engagement; no such public statements were found. 3. If transparent self-serve pricing is a procurement requirement; competitors such as OpenWeather and AccuWeather publish entry-level pricing. 4. If the requirement is a simple consumer-facing weather feed; established providers (OpenWeather, AccuWeather) may be more cost-effective and easier to integrate for basic forecasts. 5. If a fully integrated workflow platform (dashboards, alerting, collaboration) is required out of the box; some competitors provide integrated platforms rather than API-only offerings. Sources: - Silurian site (sales-led): https://silurian.ai/ - OpenWeather pricing: https://openweathermap.org/full-price - AccuWeather plans: https://developer-dev.accuweather.com/documentation/overview - Tomorrow.io platform + API: https://support.tomorrow.io/hc/en-us/articles/23554984091156-Tomorrow-io-Pricing-Overview

Deal disqualifiers (poor fit)

Organizations that require public SOC 2 or ISO 27001 documentation prior to engagement.
Organizations that require a fully packaged UI and alerting platform and do not want to build on an API-only product.
Organizations that cannot accept early-stage vendor maturity risk for mission-critical operations without robust fallback arrangements.

Publicly acknowledged limitations

In cyclone forecasting, Silurian notes that track accuracy is a primary focus and that intensity forecasting and rapid intensification remain areas for further development.

Source:

https://silurian.ai/blog/gft-cyclones

Practical limitations (not necessarily stated, but important)

- Model scope vs operational demands: enterprises commonly require probabilistic ensembles, clear uncertainty quantification, and robust fallback strategies. - Data governance for fine-tuning: when proprietary asset data are provided for model adaptation, contractual clarity is required around data use, retention, and model ownership. - Name collision risk: the company name "Silurian" is also used by unrelated entities, which can complicate diligence unless legal entity details are confirmed.

02Why they buy

Capabilities supported by public evidence

- Team and research linkage to foundation-model research (Aurora) that explores ML approaches to Earth-system forecasting; this provides technical context for Silurian’s modeling approach. - High-resolution regional model (GFT-US): vendor materials state 3 km hourly forecasts for the contiguous U.S. - A cyclone-track API (GFT-C) focused on tropical cyclone tracks, representing a targeted product offering relative to general-purpose forecast APIs. Sources: - https://www.nature.com/articles/s41586-025-09005-y - https://silurian.ai/blog/gft-us-announce - https://silurian.ai/blog/gft-cyclones

Areas where alternatives may provide advantages

Established support structures, documented SLAs, and broader trust signals.
Public pricing and clearer procurement pathways.
Integrated workflow tools (dashboards, alerting) from some competitors.

03Field reports

Review-platform presence (G2/Capterra/TrustRadius)

No public listings or reviews were found on major review platforms during this research.

This limited third-party review footprint aligns with expectations for an early-stage company but reduces independent verification of customer satisfaction.

Community/Social signals (balanced)

Public discussion about the company on forums such as Reddit and Hacker News was limited or not easily discoverable; many search results for the product name relate to unrelated topics. Developer-facing signals include published TypeScript and Python SDK documentation and package listings, which indicate some developer adoption or testing.

Vendor/partner quotes (not independent reviews)

The company’s blog includes partner quotes in product launch posts praising model improvements. Such quotes are vendor-selected and should not be treated as neutral third-party reviews.

Likely reasons organizations evaluate the product (inferred from positioning)

Lower-latency hourly forecasts and earlier availability compared to specific operational benchmarks.
Higher-resolution regional forecasting and frequent model refresh.
An approach that supports fine-tuning on customer observations to potentially improve local accuracy.

Reasons organizations might avoid or discontinue use (inferred)

Limited public track record and few third-party reviews.
Security and compliance concerns due to lack of publicly documented SOC 2 / ISO 27001 statements.
The need for explainability and robust handling of rare extreme events; machine-learning models can underperform on out-of-distribution extremes without specific safeguards.

Case study 1: Utility collaboration (icing forecasts / power grid)

The company published blog content describing fine-tuning GFT on utility-grade observations for asset-level forecasting and rime-ice alerts. Published materials include claims of day-ahead rime-ice alerts and operational framing such as reduced dispatch costs and warning windows. Critical evaluation: - Metrics are vendor-published and the excerpts provide limited methodological detail. - Reported performance metrics require context on dataset size, class imbalance, baseline comparisons, and the operational cost of false positives. - The collaboration indicates applied engagement with a utility and a use-case aligned with asset-aware forecasting.

Case study 2: Disaster-response partner (hurricane/cyclone tracking)

The company announced an operational cyclone API model and identified an initial partner in the disaster-response space.

Critical evaluation:

The partner quote in the announcement is not an independent review.
Published material emphasizes track accuracy; intensity forecasting and rapid intensification are identified as areas for further work.

Government contract signal (procurement record)

A public procurement tracker lists a government contract award associated with the vendor.

Critical evaluation:

Procurement trackers can contain errors; however, the specific contract record suggests government interest or experimentation rather than confirmation of broad production adoption.

04Best fit

Company sizes

Most plausible ICP based on the sales-led website and enterprise vertical focus:

Mid-market to enterprise organizations, and smaller organizations with intensive weather exposure (for example, trading shops or climate risk startups) that have engineering resources.

Industries/verticals with strongest fit

Utilities, grid operators, and energy retailers (load forecasting, outage management, icing forecasts)
Renewable energy operators and traders (wind and solar forecasting, scheduling)
Insurance and catastrophe-modeling platforms
Disaster response and resilience platforms

Roles/personas

Decision makers: Head of Data/AI, CTO, VP Engineering, Head of Risk, Head of Energy Trading, Operations leadership
End users: Quant/research engineers, meteorology teams, reliability engineers, data engineers

Qualification criteria (good fit)

Organizations that can run side-by-side evaluations against operational model feeds and commercial providers.
Organizations that value API-first delivery and have engineering resources for integration.
Organizations possessing proprietary observations (asset or sensor data) that could be used to improve local forecasts through fine-tuning.

05The pitch

What the product does (objective value proposition)

Silurian provides AI-driven Earth-system forecasting via an API intended to function as an infrastructure component for integrating forecasts into downstream systems. Its primary commercial product is the Earth API, which delivers global and regional forecasts produced by Silurian’s Generative Forecasting Transformer (GFT) model family, including a high-resolution U.S. regional model and a cyclone-track model. The product functions as: - A weather/earth forecasting API that returns machine-learning-generated forecasts (hourly granularity, multiple variables). - A foundation-model-based approach designed to be fine-tuned on customer data (for example, asset, sensor, or infrastructure observations) to produce forecasts intended for operational decision-making in verticals such as energy and insurance.

Target market and positioning

Silurian targets organizations with weather-sensitive operations and platforms, including:

Energy (renewables scheduling, trading, grid reliability)
Insurance (catastrophe and risk modeling)
Agriculture
Logistics / disaster response

This positioning is reinforced by accelerator materials and the company’s public messaging around asset-aware, forecast-integrated workflows.

Get a comprehensive analysis of Silurian including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →

Published Aug 14, 2026

01Common complaints

If procurement requires extensive third-party validation (for example, large volumes of G2/Capterra/TrustRadius reviews and broad analyst coverage); Silurian’s public footprint is currently limited.
If public enterprise compliance certifications (SOC 2/ISO 27001) are required before engagement; no such public statements were found.
If transparent self-serve pricing is a procurement requirement; competitors such as OpenWeather and AccuWeather publish entry-level pricing.
If the requirement is a simple consumer-facing weather feed; established providers (OpenWeather, AccuWeather) may be more cost-effective and easier to integrate for basic forecasts.
If a fully integrated workflow platform (dashboards, alerting, collaboration) is required out of the box; some competitors provide integrated platforms rather than API-only offerings.

Sources:

Silurian site (sales-led): https://silurian.ai/
OpenWeather pricing: https://openweathermap.org/full-price
AccuWeather plans: https://developer-dev.accuweather.com/documentation/overview
Tomorrow.io platform + API: https://support.tomorrow.io/hc/en-us/articles/23554984091156-Tomorrow-io-Pricing-Overview

Deal disqualifiers (poor fit)

Organizations that require public SOC 2 or ISO 27001 documentation prior to engagement.
Organizations that require a fully packaged UI and alerting platform and do not want to build on an API-only product.
Organizations that cannot accept early-stage vendor maturity risk for mission-critical operations without robust fallback arrangements.

Publicly acknowledged limitations

In cyclone forecasting, Silurian notes that track accuracy is a primary focus and that intensity forecasting and rapid intensification remain areas for further development.

Source:

https://silurian.ai/blog/gft-cyclones

Practical limitations (not necessarily stated, but important)

Model scope vs operational demands: enterprises commonly require probabilistic ensembles, clear uncertainty quantification, and robust fallback strategies.
Data governance for fine-tuning: when proprietary asset data are provided for model adaptation, contractual clarity is required around data use, retention, and model ownership.
Name collision risk: the company name "Silurian" is also used by unrelated entities, which can complicate diligence unless legal entity details are confirmed.

02Why they buy

Capabilities supported by public evidence

Team and research linkage to foundation-model research (Aurora) that explores ML approaches to Earth-system forecasting; this provides technical context for Silurian’s modeling approach.
High-resolution regional model (GFT-US): vendor materials state 3 km hourly forecasts for the contiguous U.S.
A cyclone-track API (GFT-C) focused on tropical cyclone tracks, representing a targeted product offering relative to general-purpose forecast APIs.

Sources:

Areas where alternatives may provide advantages

Established support structures, documented SLAs, and broader trust signals.
Public pricing and clearer procurement pathways.
Integrated workflow tools (dashboards, alerting) from some competitors.

03Field reports

Review-platform presence (G2/Capterra/TrustRadius)

No public listings or reviews were found on major review platforms during this research.

This limited third-party review footprint aligns with expectations for an early-stage company but reduces independent verification of customer satisfaction.

Community/Social signals (balanced)

Vendor/partner quotes (not independent reviews)

The company’s blog includes partner quotes in product launch posts praising model improvements. Such quotes are vendor-selected and should not be treated as neutral third-party reviews.

Likely reasons organizations evaluate the product (inferred from positioning)

Lower-latency hourly forecasts and earlier availability compared to specific operational benchmarks.
Higher-resolution regional forecasting and frequent model refresh.
An approach that supports fine-tuning on customer observations to potentially improve local accuracy.

Reasons organizations might avoid or discontinue use (inferred)

Limited public track record and few third-party reviews.
Security and compliance concerns due to lack of publicly documented SOC 2 / ISO 27001 statements.
The need for explainability and robust handling of rare extreme events; machine-learning models can underperform on out-of-distribution extremes without specific safeguards.

Case study 1: Utility collaboration (icing forecasts / power grid)

Critical evaluation:

Metrics are vendor-published and the excerpts provide limited methodological detail.
Reported performance metrics require context on dataset size, class imbalance, baseline comparisons, and the operational cost of false positives.
The collaboration indicates applied engagement with a utility and a use-case aligned with asset-aware forecasting.

Case study 2: Disaster-response partner (hurricane/cyclone tracking)

The company announced an operational cyclone API model and identified an initial partner in the disaster-response space.

Critical evaluation:

The partner quote in the announcement is not an independent review.
Published material emphasizes track accuracy; intensity forecasting and rapid intensification are identified as areas for further work.

Government contract signal (procurement record)

A public procurement tracker lists a government contract award associated with the vendor.

Critical evaluation:

Procurement trackers can contain errors; however, the specific contract record suggests government interest or experimentation rather than confirmation of broad production adoption.

04Best fit

Company sizes

Most plausible ICP based on the sales-led website and enterprise vertical focus:

Mid-market to enterprise organizations, and smaller organizations with intensive weather exposure (for example, trading shops or climate risk startups) that have engineering resources.

Industries/verticals with strongest fit

Utilities, grid operators, and energy retailers (load forecasting, outage management, icing forecasts)
Renewable energy operators and traders (wind and solar forecasting, scheduling)
Insurance and catastrophe-modeling platforms
Disaster response and resilience platforms

Roles/personas

Decision makers: Head of Data/AI, CTO, VP Engineering, Head of Risk, Head of Energy Trading, Operations leadership
End users: Quant/research engineers, meteorology teams, reliability engineers, data engineers

Qualification criteria (good fit)

Organizations that can run side-by-side evaluations against operational model feeds and commercial providers.
Organizations that value API-first delivery and have engineering resources for integration.
Organizations possessing proprietary observations (asset or sensor data) that could be used to improve local forecasts through fine-tuning.

05The pitch

What the product does (objective value proposition)

The product functions as:

A weather/earth forecasting API that returns machine-learning-generated forecasts (hourly granularity, multiple variables).
A foundation-model-based approach designed to be fine-tuned on customer data (for example, asset, sensor, or infrastructure observations) to produce forecasts intended for operational decision-making in verticals such as energy and insurance.

Target market and positioning

Silurian targets organizations with weather-sensitive operations and platforms, including:

Energy (renewables scheduling, trading, grid reliability)
Insurance (catastrophe and risk modeling)
Agriculture
Logistics / disaster response

This positioning is reinforced by accelerator materials and the company’s public messaging around asset-aware, forecast-integrated workflows.

Get a comprehensive analysis of Silurian including market position, competitive landscape, adoption trends, and peer benchmarks.

Download full report →