Neara builds a physics-enabled digital twin for utility and infrastructure operators, layering PyTorch, TensorFlow, and LiDAR processing on top of a modern data stack (Snowflake, BigQuery, Redshift, dbt, Airflow). The hiring acceleration across sales (18 open roles) and engineering (13) paired with active projects around customer adoption, platform deployment, and workflow embedding signals a shift from product-market fit into a scaling phase focused on widening TAM and deepening customer stickiness.
Neara operates a digital twin platform designed to help infrastructure owners—primarily utilities and grid operators—move from asset observation to scenario modeling and intervention planning. The product ingests geospatial and LiDAR data into a geometrically precise simulation model, enabling stress-testing, capex prioritization, vegetation management, and weather-resilience planning. The company is 201–500 employees, headquartered in Sydney, and actively hiring across multiple geographies (Australia, US, UK, Portugal, Turkey, Colombia, India), with particular momentum in sales and operations roles.
Neara relies on GIS/LiDAR for geospatial data ingestion, PyTorch and TensorFlow for ML modeling, and a cloud data warehouse stack (Snowflake, BigQuery, Redshift) with dbt and Airflow for pipeline orchestration. Frontend is TypeScript/JavaScript; infrastructure runs on AWS with PostgreSQL and DynamoDB for transactional layers.
Current projects span customer adoption and platform deployment to utilities worldwide, ROI metric development, integration with enterprise systems, capex prioritization workflows, and embedding the platform into daily vegetation-management and grid-hardening operations.
Neara's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.