Physical World Context Energy and Utilities Energy infrastructure lives in the physical world, but your AI does not see it. Wherobots gives energy and utility teams the context layer for grid analytics at scale: assess wildfire risk across thousands of miles of transmission lines, accelerate solar site selection, and build the spatial intelligence behind grid modernization. REQUEST A DEMO Companies Accelerating Outcomes with Wherobots and Apache Sedona Why Energy Teams Build Physical World Context with Wherobots AI built on documents, databases, and operational logs cannot see what geography does to your infrastructure. Wherobots closes that gap: wildfire risk assessment, renewable energy site selection, vegetation management, and grid modernization analytics, powered by the physical world data behind every asset in your territory. Slow wildfire risk analysis Complex renewable siting Limited grid visibility Siloed infrastructure data Manual transmission planning Slow wildfire risk analysis Industry Problem Why Wildfire Risk Analysis Takes Days or Weeks Assessing wildfire ignition probability and vegetation encroachment across thousands of miles of transmission lines takes days or weeks with traditional GIS tools, delaying critical mitigation decisions during high risk seasons. Wherobots solution How to Analyze Wildfire Risk 90% Faster Wherobots analyzes vegetation proximity, weather patterns, historical ignition data, and infrastructure conditions across your entire infrastructure territory in minutes instead of days. The platform processes raster and vector data together to identify high-risk zones requiring immediate attention, delivering 90% faster risk identification than traditional approaches.. 90% Faster Risk ID Complex renewable siting Industry Problem Why Renewable Site Selection Delays Clean Energy Deployment Evaluating optimal locations for utility-scale solar and wind projects requires analyzing multiple factors including solar radiation, wind patterns, land use, topography, environmental constraints, and grid proximity—a time-intensive process that delays clean energy deployment. Wherobots solution Accelerated Solar Site Selection Wherobots processes terabytes of satellite imagery, solar radiation data, meteorological records, and environmental constraint layers to identify and rank optimal sites for utility-scale solar and wind projects. Solar site selection that evaluates thousands of potential locations simultaneously, considering transmission proximity and hosting capacity, in hours instead of months. Millions of Sites Analyzed/minute Limited grid visibility Industry Problem Why Inaccurate Asset Data Prevents Real-Time Grid Capacity Decisions Inaccurate asset locations, incomplete spatial data assets, and batch updates to network models prevent energy teams from making real-time decisions about grid capacity, DER integration, and infrastructure investments. Wherobots solution How to Create a Spatial Digital Twin of Grid Infrastructure Create a comprehensive spatial digital twin of your infrastructure. Process billions of asset records, network topology data, and real-time sensor feeds to maintain an always-current view of grid and field conditions and capacity across all capacity levels. 100% Real-time Grid View Siloed infrastructure data Industry Problem Why Disconnected GIS, SCADA, and AMI Systems Make Predictive Maintenance Difficult Critical asset information exists in disconnected systems including GIS, SCADA, AMI, and work management platforms—making it difficult to correlate spatial data with operational performance for predictive maintenance and capital planning. Wherobots solution How to Unify Spatial Data Using 300+ Spatial SQL Functions Unify spatial data from multiple sources in Wherobots Cloud. Join asset records with real-time operational data, outage history, and maintenance records using Spatial SQL. WherobotsDB’s 300+ spatial functions cover vector and raster data, with native Spark SQL for tabular operations. 300+ Spatial Functions Manual transmission planning Industry Problem Why Manual Transmission Corridor Planning Takes Months and Bottlenecks Grid Expansion Planning new transmission corridors requires analyzing right-of-way constraints, environmental impacts, population density, and existing infrastructure across vast geographic areas—a months-long process that bottlenecks grid expansion projects Wherobots solution How to Evaluate Thousands of Transmission Routes Simultaneously Evaluate thousands of potential transmission routes simultaneously by analyzing terrain, land use, environmental constraints, and cost factors. Wherobots processes multi-terabyte raster datasets representing elevation, land cover, protected areas, and infrastructure to identify optimal corridors. 90% Time Savings “We’ve added ten times more data than earlier versions of this concept, and we can now do things with AI and on-demand analytics that have never been done before.” Eric Pollard CEO, ParGo See How Energy Teams Build AI That Sees Their Infrastructure Schedule a demo to see how leading energy companies process billions of asset records, assess wildfire risk at scale, and build the physical world context layer behind smarter grid decisions. Talk to us Energy & Utility Resources: Technical Examples and Case Studies Explore use cases, code examples, and webinars showing how energy companies solve wildfire risk, renewable siting, and infrastructure challenges with spatial analytics How to Detect Solar Panels in Satellite Imagery Using Raster Inference Learn how to extract solar panel locations from satellite and aerial imagery using SQL or Python with open-source machine learning models—useful for competitive analysis and renewable asset mapping. LEARN MORE Raster Data Processing Benchmark: Wherobots vs Google Earth Engine vs BigQuery Compare performance for raster operations, spatial joins at scale, and running Earth observation models across three leading geospatial platforms. READ MORE RasterFlow for Planetary-Scale Earth Intelligence See how RasterFlow bridges AI and physical world data to accelerate Earth observation analysis for energy and utility applications. WATCH NOW Frequently Asked Questions How does Wherobots handle billions of utility infrastructure data points at scale? WherobotsDB runs distributed spatial SQL across cloud clusters, processing petabytes of asset records, sensor data, and satellite imagery without moving data out of your cloud storage. Compute scales to workload size. Energy teams write spatial SQL and Python against their existing infrastructure data directly. Grid capacity analyses, vegetation proximity assessments, and transmission corridor evaluations that previously required overnight batch runs complete in minutes. What deployment options does Wherobots offer for energy and utility companies? Wherobots connects to AWS cloud storage, Databricks, and any Apache Iceberg catalog. Your data stays in place. Wherobots runs compute against it without migration or format conversion. Energy and utility companies with regulatory or data residency requirements can deploy as a managed cloud service, bring-your-own-cloud, or in a dedicated VPC. Wherobots is SOC 2 Type 2 attested. How much faster is Wherobots than traditional GIS for grid analytics and wildfire risk assessment? Traditional GIS platforms process spatial datasets on single machines and hit hard compute limits at the data volumes modern grid and wildfire analytics demand. WherobotsDB delivers up to 3x faster analytical queries and up to 2.5x faster spatial queries compared to the previous generation, with up to 45% better price performance. In practice, wildfire risk assessments across thousands of miles of transmission lines that previously took overnight complete in under an hour. Renewable site scoring that previously required days completes in hours. Can Wherobots process both vector and raster data for energy and utility applications? WherobotsDB processes vector data (asset locations, parcel boundaries, transmission corridors, land use polygons) and raster data (satellite imagery, elevation models, solar radiation grids, vegetation indices) in the same platform. RasterFlow runs computer vision models on satellite imagery at scale, enabling workflows like vegetation encroachment detection along transmission lines and land parcel classification for solar site selection. Both data types participate in the same spatial SQL queries, so energy teams do not need separate tools for different data formats. How quickly can energy teams see results from Wherobots? Most energy teams run a proof-of-concept in 2-4 weeks focused on a specific use case: wildfire risk mapping, renewable site scoring, or hosting capacity analysis. Wherobots is code-compatible with Apache Sedona, so existing Sedona SQL or Python workflows run on Wherobots without rewriting. WherobotsDB provides 300+ spatial functions covering vector and raster data, with native Spark SQL for tabular operations, and a managed notebook environment for fast iteration. Is Wherobots suited for real-time utility data processing? Wherobots is optimized for analytical workloads: processing large volumes of spatial data for grid capacity modeling, wildfire risk assessment, and infrastructure planning. For operational systems requiring sub-second response times, Wherobots handles the computationally intensive spatial analytics and ETL pipelines that feed into those real-time systems. Daily vegetation monitoring, hourly hosting capacity updates, and scenario analysis for grid expansion are where Wherobots delivers the most value. Get started Ready to learn more about how Wherobots can transform your energy workflows? TALK TO US START BUILDING
How to Detect Solar Panels in Satellite Imagery Using Raster Inference Learn how to extract solar panel locations from satellite and aerial imagery using SQL or Python with open-source machine learning models—useful for competitive analysis and renewable asset mapping. LEARN MORE
Raster Data Processing Benchmark: Wherobots vs Google Earth Engine vs BigQuery Compare performance for raster operations, spatial joins at scale, and running Earth observation models across three leading geospatial platforms. READ MORE
RasterFlow for Planetary-Scale Earth Intelligence See how RasterFlow bridges AI and physical world data to accelerate Earth observation analysis for energy and utility applications. WATCH NOW
