Agriculture
AI from field to shelf with full traceability and zero compliance risk
You get precision farming agents, supply chain traceability, demand forecasting, and sustainability reporting — all running on your cloud, connected to your existing systems, with governance built in.
Built on your AWS, Azure or GCP tenant. Traceability-first. Regulator-ready.
Proof of concept
To measurable ROI
AWS, Azure or GCP
The margin in agriculture is won in the data, not just the field
Your sensor network, satellite imagery, and supply chain data already contain the signals that predict yield variance, disease outbreak, equipment failure, and demand shifts. The challenge is not collecting data — it is acting on it fast enough to matter.
We build AI agents that close the loop between your data and your decisions — from irrigation scheduling and crop health alerts to supply chain traceability and sustainability reporting — with governance your certifying bodies and regulators can audit.
yield improvement reported by AI precision farming adopters
water use reduction achievable with AI irrigation agents
of global food supply chains projected to use AI traceability by 2025
reduction in over/understock with AI demand forecasting
From field sensor to compliance record — in one workflow
We connect your IoT sensors, satellite feeds, ERP, and supply chain systems to AI agents that optimise field operations, track product from origin to shelf, and generate the traceability and sustainability documentation your certifiers and regulators require
IoT sensors, satellite, ERP and supply chain
- Approval gates
- Human-in-loop
- Audit logging
AWS, Azure or GCP
- Traceability records
- Compliance reports
- Yield forecasts
What we deliver for agricultural enterprises
From precision farming to supply chain — governed, auditable, enterprise-ready
Precision irrigation
Generate field-level irrigation plans from soil and weather data.
ROI
Process
Collect field data
Soil moisture sensors, weather APIs, and crop stage observations ingested in real time
Model water requirements
AI calculates field-level irrigation needs based on crop stage, soil type, and forecast conditions
Deliver recommendations
Zone-specific irrigation schedules pushed to controllers or displayed on operator dashboards
Sources
Originally published: AWS Case Study — Netafim; GCP Case Study — John Deere — reproduced for illustrative purposes
Crop health monitoring
Spot disease and pest threats from satellite imagery early.
ROI
Process
Capture field imagery
Satellite and drone multispectral imagery collected at regular intervals across fields
Detect anomalies
Computer vision models identify disease, pest pressure, and nutrient deficiency signatures
Generate field maps
Georeferenced health maps with treatment recommendations delivered to agronomists
Sources
Originally published: AWS Case Study — Syngenta; Azure Case Study — Bayer — reproduced for illustrative purposes
Yield forecasting
Predict harvest volumes weeks ahead with field-level accuracy.
ROI
Process
Integrate historical data
Past yield records, weather patterns, and soil data combined with in-season observations
Generate forecasts
ML models produce field-level harvest volume predictions with confidence intervals
Inform planning
Early forecasts support procurement, logistics, and contract negotiation decisions
Sources
Originally published: GCP Case Study — Corteva; AWS Case Study — Olam — reproduced for illustrative purposes
Supply chain traceability
Track every batch from field to shelf with full audit trails.
ROI
Process
Connect supply chain nodes
Grower records, processing data, and logistics events linked in a unified traceability platform
Build audit trails
AI creates end-to-end provenance records for every batch from field to retail shelf
Enable rapid recall
Instant trace-back capability for food safety incidents and retailer compliance audits
Sources
Originally published: Azure Case Study — Cargill; AWS Case Study — Walmart/JBS — reproduced for illustrative purposes
Demand and procurement intelligence
Forecast demand and flag supply risks before they hit margins.
ROI
Process
Aggregate demand signals
Historical sales, seasonal patterns, weather data, and market indicators fused into demand model
Forecast and optimise
AI generates SKU-level demand forecasts and optimal procurement timing recommendations
Alert on supply risks
Early warnings on supply disruptions, price movements, and stock imbalances
Sources
Originally published: GCP Case Study — Bunge; Azure Case Study — ADM — reproduced for illustrative purposes
Food safety and contamination alerts
Flag contamination risks before they become recalls.
ROI
Process
Monitor quality data
Test results, environmental readings, and supplier data streamed from across the supply chain
Detect contamination risk
AI flags quality deviations, supplier anomalies, and environmental conditions indicating risk
Escalate with evidence
Documented alert packages with traceability data delivered to quality and compliance teams
Sources
Originally published: AWS Case Study — Nestlé; Azure Case Study — Tyson — reproduced for illustrative purposes
Sustainability reporting
Auto-generate carbon, water, and biodiversity reports.
ROI
Process
Collect ESG data
Carbon, water, energy, and biodiversity data aggregated from operational and sensor systems
Calculate and validate
AI computes Scope 1-3 emissions, water footprints, and biodiversity metrics with audit trails
Generate disclosures
Reports structured for certification bodies, regulatory filings, and investor ESG frameworks
Sources
Originally published: GCP Case Study — Syngenta; Azure Case Study — Yara — reproduced for illustrative purposes
Equipment and fleet management
Schedule machinery service before harvest-critical breakdowns.
ROI
Process
Monitor machinery data
Telematics, engine diagnostics, and usage data from tractors, combines, and sprayers
Predict failures
ML models forecast component wear and schedule service before harvest-critical breakdowns
Optimise fleet deployment
AI recommends optimal machine allocation across fields based on task and condition
Sources
Originally published: AWS/Databricks Case Study — John Deere; Azure Case Study — AGCO — reproduced for illustrative purposes
Regulatory compliance automation
Track regulation changes and produce audit-ready documents.
ROI
Process
Track regulatory changes
AI monitors pesticide, water use, and environmental regulation updates across jurisdictions
Map obligations to operations
Changing requirements automatically mapped to your specific crops, chemicals, and locations
Generate audit documentation
Compliance certificates and audit-ready reports produced for certification body review
Sources
Originally published: AWS Case Study — Bayer; GCP Case Study — Corteva — reproduced for illustrative purposes
VP, Generative AI, Bayer Crop Science
Daniel Kurdys
"Microsoft Azure gives us the scalable infrastructure to run generative AI models across our global breeding and agronomic datasets. We are accelerating trait discovery workflows that would have taken our scientists years, and making those insights actionable for growers at the field level"
Originally published: Microsoft Customer Stories — Bayer Crop Science — reproduced for illustrative purposes
How we work in agricultural and food supply chain environments
Agricultural AI is only as valuable as the data it connects. We start by mapping your existing data sources — IoT sensors, ERP, satellite feeds, supply chain platforms — and identifying the one use case where AI will create the most immediate, measurable impact.
Everything we build runs on your cloud tenant. Your grower data, your supply chain records, your sustainability data — none of it goes to a third-party platform. Your certifiers and regulators can audit the system directly.
01
Data and use case mapping
We assess your existing data sources, identify the highest-value AI use case, and map it to your traceability and compliance obligations.
02
30-day proof of concept
A working AI agent on your cloud, connected to your field or supply chain data, with demonstrable output in 30 days.
03
Governance by design
Audit trails, traceability records, and explainable outputs built in — structured to meet certification body and regulatory requirements.
04
Scale in 12–18 weeks
From one validated workflow to measurable operational ROI across your farming or supply chain operation.
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