Physical World Context for Financial Services & Insurance Your AI reads financial documents, databases, and market feeds. It cannot see which assets sit in a flood zone, which portfolios concentrate in wildfire corridors, or where climate risk hides in your book. Wherobots is the AI context engine that gives financial services and insurance teams the physical world context for property risk assessment, catastrophe modeling, and exposure analysis at portfolio scale. Know your exposure before the market does. REQUEST A DEMO Why Financial Services Teams Build Physical World Context with Wherobots AI tools built on documents and structured data cannot see what geography does to risk. Wherobots closes that gap: climate risk modeling, property risk assessment, catastrophe exposure analysis, and portfolio-level geospatial intelligence, all on the physical world data behind your assets, your exposure, and your bottom line. Climate Risk Blind Spots Across Property Portfolios Limited alternative data insights Slow assessments Fragmented portfolio intelligence Inadequate retail location optimization Climate Risk Blind Spots Across Property Portfolios Industry Problem Incomplete Climate Risk Assessments Across Property Portfolios Financial institutions need to assess climate risk across millions of properties, but AI systems built on financial documents and market data have no visibility into the physical world conditions that drive loss. Flood zones, wildfire corridors, storm surge exposure: these risks live in geography, not spreadsheets. Wherobots solution Asset-Level Climate Risk Analytics at Portfolio Scale Wherobots processes satellite imagery, flood hazard maps, wildfire risk layers, and property records at planetary scale to deliver climate risk analytics across your entire portfolio. Spatial SQL joins physical world data with your asset records so underwriters and risk analysts see exposure before it becomes a claim. 30 000+ Assets assessed Limited alternative data insights Industry Problem Physical World Signals Missing from Investment Models Satellite imagery, weather data, and geospatial datasets hold signals that predict asset performance, supply chain disruption, and market shifts. Without spatial AI infrastructure, investment teams cannot integrate physical world data into their models. Wherobots solution Geospatial AI Infrastructure for Alternative Dataa Wherobots gives investment and risk teams the geospatial AI infrastructure to process satellite imagery, mobility data, and environmental datasets at scale. RasterFlow runs computer vision models on Earth observation data. WherobotsDB joins the results with financial records in standard SQL. Physical world context becomes a structured input to your models. 100% Data coverage Slow assessments Industry Problem Property Risk Assessment at Portfolio Scale Takes Days Property risk assessment at portfolio scale takes days or weeks when legacy tools process locations one at a time. Catastrophe modeling requires cross-referencing millions of addresses against flood, wind, seismic, and wildfire hazard layers. Traditional GIS cannot keep up. Wherobots solution Catastrophe Modeling in Minutes, Not Days Wherobots processes millions of property locations against multiple hazard layers in minutes, not days. Distributed spatial joins at scale turn catastrophe modeling from a batch process into a near-real-time capability. Underwriters and portfolio managers get exposure answers faster than the market moves. 95% Time saved Fragmented portfolio intelligence Industry Problem Fragmented Risk Data Across Disconnected Systems Risk data lives in disconnected systems: property records in one database, flood maps in a GIS, satellite imagery in cloud storage, weather data in a third-party API. No single platform joins these to give portfolio managers a unified view of geographic exposure. Wherobots solution Unified Geospatial Context for Portfolio Risk Intelligence Wherobots unifies physical world datasets on a single Apache Iceberg-based platform. Spatial SQL joins property records, hazard layers, satellite-derived insights, and environmental data in one query. The result: a complete geospatial context layer for portfolio-level risk intelligence. Thousands Of holdings analyzed Inadequate retail location optimization Industry Problem Site Selection Without Spatial AI Branch placement, retail expansion, and market analysis depend on understanding foot traffic, demographics, competitor proximity, and physical accessibility. Financial institutions making these decisions without spatial AI rely on outdated reports and manual analysis. Wherobots solution Data-Driven Site Selection at Scale Wherobots processes location intelligence datasets at scale: mobility data, demographic boundaries, competitor locations, and physical accessibility layers. Spatial SQL and Python workflows score and rank candidate locations against multiple geographic criteria simultaneously. Physical world context turns site selection from guesswork into data-driven decision-making. Millions Of locations analyzed Financial Institutions and Insurers Using Wherobots and Apache Sedona “Getting data, algorithms, and compute in one place with Spark/Sedona notebooks is a huge boost. Powerful like Earth Engine, but with the control developers need to get jobs done.” John Powell Sr. Geospatial Data Engineer, Addresscloud See How Financial Services Teams Build AI That Sees Risk Schedule a demo to see how leading insurers and financial institutions process millions of property records, model catastrophe exposure, and build the physical world context layer behind smarter risk decisions. TALK TO US Resources for Financial Services and Insurance Teams Use cases, examples, and reproducible notebooks for financial services & insurance teams. Aarden.ai: 300X Faster Landholding Analysis How Aarden.ai used Wherobots to accelerate landholding analysis 300X Learn more Wherobots vs Google Earth Engine and Big Query See how Wherobots compares to Google Earth Engine and Big Query for raster operations, spatial joins at scale, and running Earth observation models Learn more RasterFlow: Planetary Scale Earth Intelligence How RasterFlow works and why you should consider it for planetary-scale Earth observation models that you want to run instantly. RasterFlow Webinar Frequently Asked Questions: Geospatial Analytics for Financial Services and Insurance How does Wherobots support climate risk modeling and property risk assessment at portfolio scale? Wherobots processes satellite imagery, flood hazard maps, wildfire probability layers, and property records in a single distributed query. WherobotsDB’s 300+ spatial functions cover vector and raster data, with native Spark SQL for tabular operations, so risk analysts can join physical-world hazard data with asset records without moving data or switching tools. Climate risk modeling that previously required days of batch processing completes in minutes at portfolio scale. Insurers and portfolio managers use the platform to score flood and wildfire exposure at the address level, model catastrophe scenarios, and monitor accumulations across their full book. We already use H3 to analyze risk concentration. How would Wherobots extend that capability? H3 indexing works well for risk score aggregation and exposure summaries at the hexagonal cell level. WherobotsDB extends that foundation by joining H3 grids with higher-fidelity vector data (parcel boundaries, building footprints, road networks), raster layers (satellite imagery, elevation models, precipitation grids), and event footprints that H3 alone cannot represent. Custom spatial factors computed from that richer dataset feed back into your risk models. Portfolio-wide spatial queries and raster sampling run in parallel at any scale. H3 stays in your workflow. Wherobots handles what H3 cannot. What physical-world data does Wherobots support for property risk assessment? WherobotsDB processes three data categories in one platform. Vector data covers property boundaries, parcel records, building footprints, flood zones, road networks, and point inventories. Raster data covers satellite imagery, aerial photography, elevation models, climate grids, precipitation layers, wildfire probability grids, and weather event footprints. Tabular data covers policy tables, claims records, and third-party model scores, handled through native Spark SQL. All three are queryable in one platform, so property risk assessment workflows do not require separate pipelines per data format. How does Wherobots connect to existing data infrastructure? Wherobots connects to AWS cloud storage, Databricks, and any Apache Iceberg catalog. Your data stays in your cloud storage. Wherobots runs compute against it without migration or format conversion. Financial institutions with compliance requirements can deploy in a managed cloud, bring-your-own-cloud, or VPC configuration. Wherobots is SOC 2 Type 2 attested. How does Wherobots compare to traditional GIS tools for catastrophe modeling? Traditional desktop GIS and single-node spatial libraries process one location at a time. Catastrophe modeling that requires cross-referencing millions of property addresses against flood, wind, seismic, and wildfire hazard layers hits compute limits at that architecture. WherobotsDB runs distributed spatial SQL across billions of rows and terabytes of raster and vector data, processing millions of locations against multiple hazard layers in minutes. The platform handles spatial joins, raster sampling, and coordinate reference system reconciliation that takes days in legacy tools. Risk teams write standard SQL and Python. The distributed compute layer handles the scale. Get Started Know your exposure before the market does. GET STARTED TALK TO US
Aarden.ai: 300X Faster Landholding Analysis How Aarden.ai used Wherobots to accelerate landholding analysis 300X Learn more
Wherobots vs Google Earth Engine and Big Query See how Wherobots compares to Google Earth Engine and Big Query for raster operations, spatial joins at scale, and running Earth observation models Learn more
RasterFlow: Planetary Scale Earth Intelligence How RasterFlow works and why you should consider it for planetary-scale Earth observation models that you want to run instantly. RasterFlow Webinar
