FIELD + GROWER
Plot boundaries, crop, season, practice, observation and outcome data.
VERIFIED DATA FOUNDATION
Bring every physical signal into one governed data layer.
Cropin Data Hub unifies field, Earth, weather, sensor, machine and enterprise data for Food-Ag, then extends the same architecture to Forest, Water, Energy and Infrastructure use cases.
THE DATA LAYER FOR PHYSICAL AI
The useful unit is not a dataset. It is an asset changing over time.
Data Hub converts fragmented observations into verified, temporal and asset-aligned context, ready for Cropin Intelligence, OrbitAI, applications and enterprise workflows.
01Connect field, Earth, machine and enterprise sourcesCONNECT
02Resolve identity, location, time and domain meaningCONTEXTUALIZE
03Preserve quality, lineage, permissions and evidenceGOVERN
04Serve trusted context to models, agents and workflowsACTIVATE
01 / PROVEN FOUNDATION · FOOD-AG
Years of agricultural context, not disconnected datasets.
The Food-Ag Data Hub understands the operating unit of agriculture: plot, crop, season, grower, practice, event and outcome. Every new signal becomes more useful because it is connected to that context.
EARTH OBSERVATION
Optical, SAR, satellite and drone imagery aligned to the asset.
WEATHER + CLIMATE
Historical, current and forecast conditions with climate context.
IOT + SENSORS
Soil, irrigation, weather-station and connected-field signals.
MACHINES + OPERATIONS
Mechanization, equipment, application and field-operation records.
SUPPLIER + PoP
Supplier, point-of-production, procurement and movement context.
SUSTAINABILITY
Practice, water, emissions, land-use and outcome evidence.
SOCIO-ECONOMIC
Regional, market and community context around production systems.
CROPIN DATA HUBOne identity for every physical asset.
PLOT · ASSET · GRID · TIME
02 / EXTENDED DATA HUB
Extend the context, not the complexity.
Add new physical-world and enterprise signals without rebuilding a separate data stack for every model, geography or workflow.
MULTI-SOURCE SIGNALSVERIFIED · TEMPORAL · ASSET-ALIGNED
OBSERVE
OPTICALSARLiDARGEDIDEM
SENSE
IoTSENSORSDRONESVIDEO + AUDIOMACHINES
CONTEXT
CLIMATESOILSUPPLY CHAINSOCIO-ECONOMICGROUND TRUTH
OPERATE
ENTERPRISE SYSTEMSCONVERSATIONSSKILL DATAWORKFLOWS
DATA HUBHARMONIZEINDEXVALIDATEGOVERNSERVE
03 / THE COMMON INDEX
Every signal needs a place, a time and an asset.
Cropin organizes data and intelligence on an appropriate spatial and temporal index, from field observations and sub-meter imagery to portfolio and regional grids.
RESOLUTION FOLLOWS THE DECISION
30 cmSUB-METER OBSERVATION
3 × 3 mHIGH-RESOLUTION GRID
10 × 10 mFIELD INTELLIGENCE
1 × 1 kmWEATHER + REGIONAL
5 × 5 kmCLIMATE + PORTFOLIO
COMMON CONTEXT
Query once. Trace the evidence across scale.
Preserve the relationship between a source signal, the physical asset it describes, the time it was observed and the decision it supports.
04 / THE MISSING LAYER
Physical AI needs a memory of reality.
Models can reason only from the context they receive. Cropin creates a verified, temporal record of the assets, conditions and events that applications and agents act upon.
APPLICATIONSENTERPRISE AGENTSROBOTICS / EMBODIED AIFOUNDATION MODELS
VERIFIED PHYSICAL-WORLD INTELLIGENCECROPIN INTELLIGENCE + ORBITAI
GOVERNED DATA FOUNDATIONDATA HUB
Ground · Earth · weather · machine · enterprise · human context
05 / FROM SOURCE TO DECISION
One lifecycle. Evidence preserved end to end.
Reduce repeated data engineering while improving the traceability of every signal used by an application, model or agent.
- 01
CONNECT
Enterprise, partner, field, machine and Earth-observation sources.
- 02
HARMONIZE
Standardize formats, units, identities, geography and time.
- 03
VALIDATE
Track quality, source, lineage and confidence.
- 04
INDEX
Associate every signal with the right asset, grid and event.
- 05
SERVE
Deliver governed context to models, APIs, applications and OrbitAI.
06 / IN PRACTICE
Organize data around the decision, not the department.
The same architecture supports proven Food-Ag workflows and selected physical-world extensions, with maturity stated clearly for each domain.
PLOTCROPWEATHERYIELD
DATA HUB
PRODUCTION + SOURCING CONTEXT01 / FOOD-AG · LIVE FOUNDATION
Which plots, origins or suppliers are moving away from plan?
Connect crop, field, Earth, weather and operations data to create a trusted production record from plot to regional portfolio.
ASSETHISTORYCLIMATEEXPOSURE
DATA HUB
UNDERWRITING + PORTFOLIO EVIDENCE02 / BANKING + INSURANCE
What has changed across agricultural exposure since underwriting?
Link farm identity, production history, climate, water and current-season evidence for monitoring, scenarios and early-warning workflows.
CANOPYSARDEMWATER
DATA HUB
CHANGE + RISK EVIDENCE03 / FOREST + WATER · MODELS AVAILABLE
Where is land cover, tree condition or water stress materially changing?
Combine optical, SAR, elevation, climate, ground truth and enterprise boundaries into a traceable monitoring layer.
ASSETVEGETATIONWEATHERTERRAIN
DATA HUB
INSPECTION + OPERATIONAL PRIORITY04 / ENERGY + INFRASTRUCTURE · CO-DESIGN
Which distributed assets should be inspected first, and why?
Bring asset, inspection, vegetation, weather and terrain data into a common context for upcoming utility and infrastructure workflows.
07 / FREQUENTLY ASKED QUESTIONS
What enterprise data and AI teams ask.
Clear answers on scope, quality, architecture, governance and how Data Hub connects to Cropin Intelligence and OrbitAI.
What is Cropin Data Hub?
Cropin Data Hub is the governed data foundation of Cropin Cloud. It connects, harmonizes and indexes field, Earth, weather, sensor, machine and enterprise data so applications, models and agents can work from consistent physical-world context.
What is the difference between the Food-Ag and Extended Data Hub?
The Food-Ag Data Hub is the proven foundation built around plots, crops, seasons, growers, operations and production outcomes. The Extended Data Hub applies the same governed architecture to additional physical-world signals and assets across Forest, Water and co-designed Energy and Infrastructure use cases.
How is this different from a data lake or warehouse?
A lake or warehouse stores data. Cropin Data Hub adds asset identity, spatial and temporal indexing, domain semantics, validation, lineage and delivery patterns required to make physical-world data usable by models and workflows.
How is data quality and lineage handled?
Source, timestamp, geography, asset association, transformations and validation status can be preserved so downstream teams can understand what evidence supports a model output or decision.
What spatial resolutions can the hub support?
The appropriate resolution depends on the source and decision, from sub-meter and plot-level observations to regional grids. Cropin can organize data across resolutions while maintaining a consistent asset and time context.
Can it connect with our existing systems?
Yes. The hub can connect with enterprise applications, APIs, files, IoT and sensor feeds, Earth-observation sources and partner systems. Integration and access patterns are configured around the enterprise architecture and permissions.
Does Cropin use our data to train shared models?
Data usage, tenancy, model access and retention are governed by the customer agreement and deployment design. Enterprise data is handled according to the agreed security, privacy and authorization controls.
How does Data Hub work with Cropin Intelligence and OrbitAI?
Data Hub provides trusted context. Cropin Intelligence converts that context into domain signals and predictions. OrbitAI reasons across the evidence and enterprise rules to explain conditions and prepare the next governed action.