McAi

Industry

Agriculture & food

Farms, processors, and food supply — thin margins, volatile inputs, growing demand.

Where we start

Agriculture operates on biological timescales with financial margins that leave little room for error. Input costs, weather, and yield variability dominate the result.

The data now exists to manage that variability: field-level imagery, machinery telemetry, weather, and soil sensing. Turning it into decisions requires models that respect the agronomy, and interfaces that a farmer will actually use during a working day.

We work on yield, input efficiency, processing, and food supply chains, with an emphasis on adoption rather than novelty.

Typical results

-14%
input cost per tonne on managed hectares
+8%
processing yield at a food manufacturer
1 season
to demonstrate field-level value

Illustrative client outcomes, representative of engagements in this sector.

Talk to this practice

How we help

Yield and inputs

Variable-rate application and field-level forecasting that reduce input cost per tonne.

Processing

Quality and throughput models for mills, dairies, and packing operations.

Traceability

Supply-chain provenance systems that satisfy regulatory and retail requirements without adding paperwork.

Sustainability

Measurement of emissions, water, and soil outcomes in a form that buyers and regulators accept.

Evidence

What this looks like in practice

-14%
input cost per tonne on managed hectares
+8%
processing yield at a food manufacturer
1 season
to demonstrate field-level value

Related insights

Next step

Bring us your agriculture & food problem

A ninety-minute working session is usually enough to agree the baseline, the owner, and the first evidence gate.