Agriculture and food production.

From field to shelf, with fewer surprises.

Agriculture

01The situation

Yields depend on weather, soil and timing; food plants depend on raw material of uneven quality and volume. Satellite data, field sensors and plant records exist, but nobody connects them to the planning decision.

02Where AI agents help
Use caseWhat the agent doesDataWho gets the result
Crop monitoring and yield forecastingTracks crop condition field by field from satellite imagery and weather and forecasts yield weeks before harvestSentinel or commercial imagery, weather, field recordsAgronomists, management
Harvest and logistics planningTurns the yield forecast into a harvest, transport and storage planForecast, fleet and elevator capacityOperations
Raw material and production planningBalances incoming raw material, recipes, line capacity and orders for a food plantERP, MES, supplier dataPlant planners
Quality and traceabilityLinks every batch to its raw material, process parameters and lab results; answers audit questions in minutesLIMS, MES, ERPQuality, compliance
Subsidy and reporting assistantPrepares reports for state programmes and agricultural statistics from the data already in your systemsERP, field recordsFinance, compliance

These are capabilities we offer, not a list of delivered projects.

03Deployment

Agroholdings and food producers usually run in the cloud or in a Kazakhstan data centre; satellite processing needs scalable compute.

04A typical first project

A pilot on one crop and one region for a season, measured against the actual harvest.