Environmental intelligence.
Bring your datasets or stream live observations. Run them through multimodal models and agents. Get analysis, predictions, and decisions you can act on.
From raw observation to decision.
Bring your data
Upload datasets, connect live sensor or satellite streams, point at an existing database. CSV, GeoJSON, NetCDF, Parquet, time-series APIs.
Run multimodal models
Text, vision, geospatial. Summarize a field report, classify a satellite tile, forecast a metric, score an anomaly. Bring a model or use a hosted one.
Get predictions & decisions
Forecasts, anomalies, classifications, recommendations. Outputs are typed and grounded — every answer links back to the inputs that produced it.
Work the way your team works.
Console
Build flows in the browser. Point at a dataset, pick a model, wire an agent, schedule a run. No setup.
SDKs
TypeScript and Python. Same primitives as the console — datasets, models, agents, runs — typed end to end.
API
Stable HTTP surface for everything you do in the console or SDK. Auth via keys; outputs are JSON.
Predefined or custom.
Start with agents that already know how to summarize, classify, forecast, and audit environmental data. When you need something specific, define your own — same primitives, same surface.
Compose them into pipelines: ingest, transform, model, decide, route. Run on a schedule, on a trigger, or on demand. Every step is observable and reproducible.
Predefined agents
Summarizers, classifiers, anomaly scorers, report writers. Ready to point at your data.
Custom agents
Define instructions, tools, and inputs. Versioned. Yours.
Orchestration
Chain agents into flows. Schedule, trigger, branch on output.
Grounded outputs
Every answer carries its sources. Nothing fabricated.