Tilebox 2.0: Give AI Agents Traceable Geospatial Reasoning
One API for humans and agents to build, run, debug, and scale geospatial workflows across environments. Full traceability from satellite data to final output.
Try Tilebox
One API for humans and agents to build, run, debug, and scale geospatial workflows across environments. Full traceability from satellite data to final output.
Try Tilebox
Tilebox 2.0 is built for geospatial analysts, EO engineers, and defense/intelligence teams who need AI agents to work with satellite data without losing the audit trail. If your workflow involves feeding large earth observation datasets into agentic pipelines (Claude Code, OpenAI Codex, or custom agents), this is the middleware layer that makes those outputs traceable and reproducible. At $99/month for the Basic tier, it is a reasonable entry point for teams already invested in geospatial infrastructure; solo hobbyists or teams not working with satellite data will find little reason to subscribe.
Subscription-based. No free tier currently available.
What makes Tilebox 2.0 distinct in the geospatial AI stack.
Every step an AI agent takes, from data source selection to processing logic, is logged and auditable. You can trace exactly how a conclusion was reached, which eliminates the black-box problem that plagues most agentic EO workflows.
Ships with a Model Context Protocol server that lets agents like Claude Code and OpenAI Codex natively query geospatial APIs. This is the "skill layer" that teaches general-purpose agents how to reason about satellite data.
Automated workflow logging captures every API call, data transformation, and intermediate result. Think of it as structured telemetry for geospatial pipelines, not just error logs.
Run the same workflow locally, on your cloud, or across hybrid infrastructure without rewriting code. Tilebox abstracts the environment layer so you can develop on a laptop and deploy to a cluster.
A unified API to query and access satellite datasets from multiple providers. Agents and humans use the same interface, which reduces the integration burden when working across Sentinel, Landsat, or commercial imagery.
Supports streaming and real-time ingestion of geospatial data, which matters for monitoring use cases like wildfire tracking, maritime surveillance, or agricultural change detection where latency is critical.
Tilebox is specialized. Here is where it fits and where it does not.
Defense, intelligence, and commercial EO analysts who need to prove how a geospatial conclusion was derived. The traceability layer is the core value proposition here. If your outputs need to survive an audit or a briefing, this is the tool.
Developers connecting Claude Code, Codex, or custom LLM agents to satellite APIs. Tilebox's MCP server and skill layer mean you do not have to build the geospatial reasoning bridge yourself. It handles the translation between general-purpose agents and domain-specific EO data.
Teams managing complex satellite data pipelines across multiple providers and environments. The multi-environment support and unified Data Catalogs API reduce the operational overhead of stitching together Sentinel, Landsat, and commercial imagery sources.
Organizations running continuous monitoring for agriculture, maritime, environmental compliance, or disaster response. The real-time data handling and automated pipeline capabilities support always-on workflows that need to react to new imagery as it arrives.
What to know before committing.
There is no free plan to test with. At $99/month minimum, you are committing budget before you can fully evaluate whether the platform fits your pipeline. For individual researchers or early-stage startups, this is a meaningful barrier. A sandbox or limited free tier would go a long way.
Large-scale integrations involving multiple data providers, custom cloud environments, and distributed agent fleets require significant configuration. The documentation is improving, but expect a non-trivial onboarding period if you are wiring this into an existing enterprise stack. Enterprise-tier customers get on-site training for a reason.
This is a geospatial-specific tool. If you are building general-purpose AI workflows that do not involve satellite imagery, location data, or earth observation, Tilebox offers little value. The skill layer and data catalog are purpose-built for EO. That is a strength for the target audience, but it means this is not a general orchestration platform.
Tilebox is still a relatively young platform. The integrations list (MCP Server, CLI, Data Catalogs API) is solid but not deep. Compared to established geospatial platforms like Google Earth Engine or Planet's API ecosystem, the third-party integration library is thin. You may need to build custom connectors for less common data sources.
Connect your agentic workflows to Tilebox's skill layer and get traceable outputs from satellite data.