Neural Earth provides a cloud‑native spatial analytics platform that ingests satellite imagery, sensor feeds, and user‑uploaded datasets, then applies AI‑driven hazard models to generate actionable risk scores for assets. The platform automates preprocessing, supports batch and real‑time spatial compute through REST and streaming APIs, and integrates directly with risk‑management and asset‑valuation tools used by insurers, reinsurers, REITs, and infrastructure managers.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations that need to evaluate climate‑related risk face fragmented geospatial datasets, siloed hazard models, and manual remote‑sensing workflows that are slow, error‑prone, and difficult to scale across large portfolios.
Solution
Neural Earth delivers a unified spatial‑analytics platform that ingests satellite imagery, sensor feeds, and proprietary data, then applies AI‑driven hazard models and feature‑extraction pipelines to generate actionable risk scores. The engine automates pre‑processing, runs large‑scale batch jobs, and supports real‑time spatial compute through REST and streaming APIs. Users can query the system with plain‑language geospatial requests, receive AI‑enhanced insights, and integrate results directly into existing risk‑management or asset‑valuation tools. By consolidating data, models, and compute in a single cloud‑native environment, the platform reduces analysis latency from weeks to seconds while maintaining scientific rigor.
Target Audience
Primary customers are property‑and‑casualty insurers, reinsurance carriers, real‑estate investment trusts, and infrastructure asset managers that require high‑resolution geospatial risk intelligence for portfolio underwriting and resilience planning.
Features
- Built‑in Neural Earth Catalog offering curated satellite imagery and environmental layers, with support for user‑uploaded datasets via secure ingestion pipelines.
- Automated pre‑processing and feature extraction using integrated eye‑tracking‑free remote‑sensing workflows.
- Hazard and climate scoring models (flood, wildfire, wind, heat) powered by the Gaia AI engine, configurable for custom perils.
- Scalable ML pipelines that handle batch processing of millions of assets and support incremental model retraining.
- Real‑time spatial compute exposed via RESTful endpoints and WebSocket streaming for low‑latency integration.
- High‑performance rendering engine and SDKs (Python, JavaScript) for visualizing heat maps, exposure dashboards, and scenario analysis.
- API‑first architecture with FHIR‑compatible data export and role‑based access controls for enterprise security compliance.