Physical World Context for Mobility & Map Making AI built on documents and databases does not understand roads, routes, or the physical geography that shapes movement. Wherobots gives mobility and mapping teams the context layer to process billions of GPS points, build location intelligence products, and keep map data fresh at planetary scale. REQUEST A DEMO Companies Accelerating Outcomes with Wherobots and Apache Sedona Why Mobility Teams Build Physical World Context with Wherobots Mobility data is physical world data. Every GPS trace, every trajectory, every map feature exists in geography. Wherobots provides the geospatial AI infrastructure to process billions of location data points, execute spatial joins at scale, and deliver the location intelligence products your customers depend on. Expensive operations Inaccurate outputs Complex joins Siloed data Fixed Infrastructure Expensive operations Industry Problem Trajectory Processing Takes Hours or Days at Scale Trajectory processing takes hours or days when analyzing billions of GPS points across millions of devices, delaying location intelligence products and fleet optimization insights Wherobots solution Process GPS Trajectories Up to 20x Faster with Distributed Spatial Processing Wherobots processes trajectories up to 20x faster, handling billions of GPS points in minutes using distributed spatial processing. Wherobots turns raw GPS streams into structured location intelligence, giving your AI stack the physical world context it needs to reason about movement, routes, and geographic patterns. 20× faster Inaccurate outputs Industry Problem Raw GPS Traces Fail to Align Accurately to Road Networks Raw GPS traces contain positioning errors and signal drift that prevent accurate map alignment. Without proper map matching, mobility data products produce incorrect routes, unreliable turn-by-turn data, and flawed fleet tracking — directly impacting the quality of location intelligence delivered to customers. Wherobots solution Achieve 99.8% Route Accuracy with Distributed Map Matching Transform raw GPS traces into accurate routes with Wherobots’ distributed map matching processing millions of trajectories against road networks. 99.8% Route Accuracу Complex joins Industry Problem Complex Spatial Joins Across Millions of POIs Overwhelm Traditional Systems Foot traffic analysis across millions of POIs requires complex spatial joins that overwhelm traditional data systems and even modern warehouses Wherobots solution Run Spatial Joins Across Millions of POIs Simultaneously Wherobots processes movement patterns across millions of points of interest simultaneously using distributed spatial joins — operations that overwhelm traditional data systems and modern warehouses. What once required complex infrastructure workarounds runs natively at scale on the Wherobots spatial lakehouse. Processing efficiency Billions of POIs in seconds Siloed data Industry Problem Multi-Source Mobility Data Stays Siloed When Platforms Can’t Integrate GPS, Raster, and Vector Data at Scale Multi-source mobility data remains siloed when platforms can’t efficiently integrate GPS traces, road networks, building polygons, telematics data, raster data, and geospatial regional context at scale in a single platform. Wherobots solution Unify GPS, Vector, and Raster Data in a Single Spatial Lakehouse Wherobots unifies mobility data, including GPS traces, road networks, building polygons, telematics, and raster data — with native support for trajectories, vector data, and raster formats on a single lakehouse platform powered by Apache Sedona and Iceberg. 100% Unified Fixed Infrastructure Industry Problem Fixed Infrastructure Forces Mobility Teams to Over-Provision and Overpay Traditional spatial infrastructure requires fixed compute provisioning, forcing mobility data teams to size for peak workloads and pay for idle capacity. As data volumes grow, scaling requires costly re-architecture rather than elastic expansion, creating a ceiling on what teams can process and a floor on what they must spend. Wherobots solution Reduce Infrastructure Costs by Up to 70% with Serverless Spatial Computing Wherobots deploys as a serverless managed cloud service, scaling compute elastically with your workload. Mobility teams pay only for what they use, eliminating idle capacity costs and enabling up to 70% in infrastructure savings compared to fixed spatial computing environments. 70% In common savings “With Apache Sedona, we process millions of fleet-derived traffic signs, using scalable spatial joins and partitioning to automate map updates—enhancing Amazon Last Mile’s delivery networks for faster, more reliable routing.” Arka Pratim Das Sr. Manager, Software Development, Amazon Maps See How Mobility Teams Build AI That Understands Spatial Data Schedule a demo to see how leading mobility and mapping companies process billions of GPS points, build location intelligence at scale, and deliver fresh geospatial data products to market faster. TALK TO US Mobility & Map-Making Resources: Geospatial Analysis, Map Matching, and Spatial Data Processing Explore guides, webinars, and blog posts on geospatial data processing, map matching, and spatial analytics for mobility and map-making companies. Customer Story Accelerating Geospatial Analysis with Wherobots and GeoPostcodes Learn how Wherobots improves processing performance for postcode-based spatial analysis using distributed geospatial computing. LEARN MORE Map Matching Map Matching Webinar: GPS Trajectory Processing and Road Network Alignment Watch this webinar to learn how WherobotsAI processes millions of GPS trajectories against road networks for accurate route reconstruction and location intelligence. LEARN MORE Map Matching Map Matching for Telematics: How WherobotsAI Aligns GPS Data to Road Segments Read how Wherobots uses distributed map matching to transform raw telematics and GPS data into accurate road segment alignments for mobility data products. LEARN MORE Frequently Asked Questions: Wherobots for Mobility Data Companies How does Wherobots help mobility companies build location intelligence products? Wherobots processes billions of GPS points, trajectory datasets, and location data streams using distributed spatial SQL and Python. Teams build location intelligence products by joining movement data with road networks, points of interest, and administrative boundaries in a single query. Map freshness, route accuracy, and fleet analytics that previously required expensive batch pipelines run at scale in minutes. We already use H3 for spatial indexing. Does Wherobots add value on top of that? H3 indexing works well for discretizing spatial data and aggregating at fixed resolution. WherobotsDB extends that foundation by handling the spatial joins, trajectory analytics, and complex geometric operations that H3 alone cannot perform. Teams keep H3 for the indexing layer and use Wherobots for the full portfolio of mobility analytics: map matching, trajectory processing, POI analysis, and geospatial data mapping at scale. What types of mobility data does Wherobots support? WherobotsDB processes GPS traces, mobile device location data (bidstream and app-derived), IoT sensor streams from vehicles and infrastructure, vehicle telemetry, and trajectory datasets. Vector data (road networks, POI records, administrative boundaries, building footprints) and tabular data (fleet records, device identifiers, event logs) participate in the same spatial queries. No separate pipelines per data type. What deployment options does Wherobots offer for mobility 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. Managed cloud, bring-your-own-cloud, and VPC deployment options are available. Wherobots is SOC 2 Type 2 attested. How does Wherobots compare to traditional GIS tools and cloud data warehouses for mobility analytics? Traditional GIS tools were not built for billion-point GPS datasets or real-time trajectory processing. Cloud data warehouses handle tabular operations well but lack the spatial join performance and geometric precision that mobility analytics require. WherobotsDB runs distributed spatial SQL optimized for spatial operations, processing trajectory datasets and geospatial joins at a scale that general-purpose analytics platforms cannot match. Mobility teams write standard SQL and Python. Wherobots handles the distributed compute. How does Wherobots support location analytics and geospatial data mapping at scale? WherobotsDB provides 300+ spatial functions covering vector and raster data, with native Spark SQL for tabular operations. Mobility and map-making teams use these for location analytics workflows: map conflation, spatial joins between GPS data and road networks, point-in-polygon analysis across millions of POIs, and geospatial data mapping pipelines that run on a defined schedule. Wherobots Jobs automate recurring location analytics runs without manual intervention. 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Customer Story Accelerating Geospatial Analysis with Wherobots and GeoPostcodes Learn how Wherobots improves processing performance for postcode-based spatial analysis using distributed geospatial computing. LEARN MORE
Map Matching Map Matching Webinar: GPS Trajectory Processing and Road Network Alignment Watch this webinar to learn how WherobotsAI processes millions of GPS trajectories against road networks for accurate route reconstruction and location intelligence. LEARN MORE
Map Matching Map Matching for Telematics: How WherobotsAI Aligns GPS Data to Road Segments Read how Wherobots uses distributed map matching to transform raw telematics and GPS data into accurate road segment alignments for mobility data products. LEARN MORE
