Methods

AI that keeps an evidence trail.

AgriAI separates raw evidence, transformed features, models and customer-facing predictions so a result can always be reconstructed.

Acquire

Source adapters collect public, contracted or customer-authorized evidence.

Validate

Schema, geometry, units, quality and rights gates run before use.

Link

Evidence joins stable field, zone, season and observation identities.

Model

Baselines, temporal models and causal methods are versioned separately.

Calibrate

Intervals and performance are tested across geography, crop and horizon.

Publish

Only governed metrics reach a product, API or public page.

MODEL MANIFEST

A prediction is a permanent record.

The July 15 forecast remains distinct from the August 1 revision. Both are later joined to measured outcomes so AgriAI can answer the only accuracy question that matters: how did this model perform here, at this point in the season?

prediction_id: yn-wheat-field17-2027-07-15
model: YieldNow Wheat v1.7
training_cutoff: 2027-07-10
interval: conformal_80
features: field17-2027-07-15
status: illustrative_demo
overwrite: false

Build your evidence layer

Start with the public picture. Add your field when you are ready.

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