Tilebox-skilled agents offer efficient, verified, reproducible geospatial workflows to diversify and scale climate analytics for timely action.
The climate tech conundrum
Climate risk analytics, extreme-weather forecasting, water and nature solutions was the fastest-growing segment in climate tech in 2025, its share of funding climbing from 5.0% to 8.2% in a single year on a projected $5.5 billion (Net Zero Insights, State of Climate Tech 2025). Insurers, asset managers, and corporates filing TCFD and IFRS S2 disclosures are all hungry for asset-level risk insight.
And yet, while US climate-tech investment reached $29 billion in 2025, a record 52% of venture-backed climate-tech companies cut their net burn year-over-year just to stay resilient (Silicon Valley Bank, Future of Climate Tech 2026). The promise is enormous, but the economics are unforgiving. The difference between the two is almost always engineering overhead.
Fragile scaffolding
The geospatial market sorts into three tiers. At the bottom sit the EO satellite operators (Planet, ICEYE, Vantor) plus public archives from NASA and ESA, supplying raw imagery. In the middle, hyperscalers like Google, Microsoft, and AWS store the petabytes and ship powerful geospatial foundation models. At the top is the intelligence layer: the companies that fuse EO data with weather records, economic data, and proprietary models to deliver something a risk manager can actually act on.
That top layer is where most of the value, and complexity, concentrates. It's also where the engineering burden is heaviest, because intelligence-layer companies inherit every messy seam between the layers below them: incompatible formats, authentication to a dozen archives, and enormous files that cost real money every time they cross a cloud region.
The egress math is not hypothetical — at standard cloud pricing of roughly $0.09/GB, moving a single large embedding dataset out of one provider has run teams tens of thousands of dollars in transfer fees alone, and bespoke formats compound that tax across every downstream product. Not to mention, value-killing latency which adds to the challenge of these climate orgs making enough to keep the lights on.
Where foundation models stop and local intelligence begins
Geospatial foundation models from Google, IBM (Prithvi), Microsoft (Aurora), and Clay have been a genuine leap, compressing raw imagery into representations downstream apps can use at a fraction of the previous compute cost. But even as these models get richer, the value in climate intelligence is rarely global. It's regional and specific: this watershed, this county's flood exposure, this cooperative's fields.
On independent benchmarks, a model that performs competitively on familiar data can collapse to under 20% accuracy when moved to a new region (PANGAEA). Parametric insurance runs into the same wall from the other direction: the events it pays out on are inherently local, so a model that's only right on average is wrong exactly where a claim is filed.
Delivering actionable value takes local sensor data, regional context, validation against ground truth, and constant re-tuning, without a custom build each time. The hard part isn't running the model. It's feeding it the right local data continuously, at the granularity a region demands. And that is an orchestration problem.
Fixing what's broken
Three failure modes recur across climate data companies, and none of them are about the science. These are the common challenges across geospatial development that Tilebox was founded to solve.
Pipeline sprawl. Every new product pulls from different sources in different formats, so each becomes a fragile custom pipeline to maintain, and the cost of a product line is dominated not by the model but by the connectors feeding it.
The geospatial data tax. Imagery files are huge, moving them between regions burns egress fees, and wiring up connectors takes weeks of engineering before a single insight ships — overhead that, for a small team, is the difference between profitability and another bridge round.
The last-mile workflow gap. Since finance runs on tables and feeds rather than maps, insight that can't drop into the format and cadence a client already works in doesn't matter how good the underlying data is.
To fix these problems, you need both interoperability and speed. That's why we released Tilebox for agentic engineering, and built it to support any data source, environment, algorithm, and coding agent. More importantly, when using Tilebox, agents work more efficiently.
Agents that run the pipeline, not just the model
The difference between an AI answer and an operational workflow is whether you can inspect, reproduce, and trust the result. Agents on Tilebox use the same operational context human developers do, through three primitives:
MCP server exposes live dataset schemas, query tools, and job status. The agent reads the real schema (exact fields like
cloud_cover,precise_time) instead of guessing from stale docs, so its code runs first time rather than failing on an incorrect field.CLI machine-readable descriptions of every command and return. The agent runs real, governed actions (discover data, trigger a workflow, inspect execution) then parses the result and repeats the loop. No human stitching steps together; the run is reviewable from the terminal.
Agent skills a single-line install drops them into the universal skills directory your coding agent already reads (Claude Code, Cursor, Codex). No special prompt prefix: install once, describe the task in plain language.
The result: agents act through real commands against governed data, leaving a record of what ran, where, on which data, and what it produced.
Grounded that way, a climate analytics company's data operations can run largely autonomously: agents query open (or custom) datasets, deploy risk and monitoring pipelines, adjust model thresholds, and answer "are the GPU tasks stuck on the cloud cluster?" from live task state instead of a manual log dig.
And because Tilebox's architecture is inherently built for distributed computing, agents catalog and transform data without moving it. For a marketplace model, that autonomy extends to the catalog itself: agents can onboard and validate new datasets far faster than a manual review queue allows.
Start building
Here's a change detection tracker we built with an agent. Watch the demo video to see how to build the workflow underneath then run it yourself or fork the source code.
