Marissa Moore recently wrote a smart piece arguing that “Palantir for X” is inevitable, but not enough. The phrase captures something important happening in enterprise AI: every major industry is going to need software that understands its own domain deeply. Healthcare needs systems that understand patients, providers, claims, clinical workflows, drugs, labs, and care pathways. Energy needs systems that understand wells, pipelines, land, weather, equipment, sensors, maintenance, and operational risk. Supply chain needs systems that understand facilities, ports, shipments, routes, vendors, inventory, disruptions, and demand.
That framing is exciting because it points to a real platform shift. For years, enterprise software mostly helped humans organize work. The next generation has to help AI systems understand work. That requires more than a chatbot, more than a dashboard, and more than another data warehouse. It requires a living knowledge layer that can connect an enterprise’s systems, data, assets, workflows, and external signals into something machines can reason over.
That is the idea behind SeerAI. SeerAI was built around the belief that the next great enterprise software companies will not simply sell applications. They will create the context layer that lets many applications, agents, models, and digital twins operate from the same trusted understanding of the world. In other words, “Palantir for X” is the right instinct. The next step is making the architecture open, reusable, spatiotemporal, and AI-native.
Most companies already have the data they need. The problem is that the data is scattered across systems that were never designed to work together. An energy company may have GIS data in one place, inspection records somewhere else, imagery in a cloud bucket, sensor feeds in another system, asset metadata in a database, weather from external providers, and work orders in a legacy application. A supply chain company has the same issue across facilities, ports, routes, carriers, inventory, weather, geopolitical risk, and customer commitments. A government agency faces the same challenge across ISR, logistics, infrastructure, emergency response, and classified mission data. The data exists. The context does not.
That is why AI strategies keep running into the same wall. The model may be powerful, but it cannot reason over data it cannot find. It cannot connect signals that live in disconnected systems. It cannot understand a real-world asset if the relevant information about that asset is spread across ten databases, three vendors, two GIS platforms, and a pile of documents. It cannot operate a useful digital twin if the twin has no coherent understanding of space, time, entities, events, and relationships.
SeerAI’s platform, Geodesic, solves this problem by creating the context layer for physical-world AI. Geodesic connects to data where it already lives. It does not require customers to rip out existing systems or move everything into one new central repository. Instead, it creates a federated layer across enterprise systems, geospatial platforms, APIs, imagery stores, cloud environments, databases, and external data providers. It then organizes that data into a reusable knowledge graph, semantic layer, and ontology that make the information understandable to people, applications, and AI agents.
That matters because the physical world is inherently spatiotemporal. Assets are somewhere. Events happen at a time. Risk changes across geography. Supply chains move. Weather shifts. Infrastructure degrades. Sensors report continuously. Field conditions evolve. The real world cannot be understood as static rows in a table. It has to be understood through relationships across space and time. That is what Geodesic is designed for: a federated spatiotemporal data mesh, a knowledge graph that holds the relationships, and a compute layer that reasons across all of it at operational speed.
That foundation can power many products. A user can ask Geodesic to find the right data across systems without knowing where it lives. A user can ask it to generate a map or geospatial product from a natural-language request. A company can use it to map how its assets, places, risks, sensors, and workflows relate to each other. A digital twin can use Geodesic as the data and context foundation underneath it. An AI agent can use it to ground its reasoning in real operational data rather than generic text.
The applications are valuable, but the foundation compounds. A single AI workflow may solve one problem. A context layer makes the next hundred workflows easier to build. Once an enterprise has a shared understanding of its assets, locations, events, risks, vendors, sensors, and operations, every new AI application becomes faster to deploy and more trustworthy. Every model has better grounding. Every agent has better context. Every digital twin becomes more real.
That is why the “Palantir for X” concept is so powerful. Palantir proved that data integration, ontology, workflow, and operational decision-making can become a massive enterprise platform. The next generation of that idea will be broader, more open, more interoperable, and more deeply connected to the physical world. SeerAI is built for that next generation.
We do not believe every industry needs a closed, bespoke, services-heavy platform that replaces everything else. Enterprises already have cloud platforms, GIS systems, data warehouses, analytics tools, AI models, and vertical software. What they need now is the layer that makes all of those systems work together. That is the product SeerAI is building: Knowledge-as-a-Service for the physical world.
In energy, that knowledge layer understands wells, pipelines, compressor stations, rights-of-way, weather, terrain, sensors, inspections, maintenance history, and operational risk. In supply chain, it understands ports, facilities, routes, carriers, inventory, weather, disruptions, and delivery commitments. In insurance, it understands properties, hazards, claims, imagery, exposure, infrastructure, and loss history. In defense and intelligence, it understands entities, places, sensors, missions, terrain, movement, and time-sensitive operational context.
The verticals differ, but the underlying need is the same. Every organization needs its data to become usable, contextual, and actionable. Every AI system needs a trusted foundation. Every digital twin needs a living knowledge graph. Every agent needs access to the systems, places, assets, events, and relationships that define the world it is supposed to reason about.
Marissa’s “Palantir for X” framing is right because it recognizes that vertical AI needs more than software wrappers. It needs deep domain understanding. SeerAI’s view is that the fastest path to that future is not to rebuild every vertical from scratch, but to create the context infrastructure that every vertical can use. The model is important. The application is important. But the enduring enterprise value will sit in the context layer. That is what SeerAI already does.