Capability layer 3 of 3

AI-driven optimisation and decision intelligence

Your growing history, harvest forecasts, lighting costs and customer demand, weighed before the crop plan changes. Nothing in this layer controls equipment.

Overview

How it fits your operation

This layer turns your records into decisions: the conditions behind your stronger cycles, harvest forecasts that show their method and confidence, and lighting costs priced against your tariff. It informs the next crop plan. It never sends a command to equipment.

Tariff rate history in BeeGrow AI: thirty days of half-hourly electricity prices in pence per kilowatt hour, comparing a flat fixed tariff against an agile tariff whose rate swings between below zero and roughly 48 pence.
Tariff rate history · historical supplier data on a development instance, not a current quote
Why it matters

What changes on site

The conditions behind your best yields

Where cycles have finished with a recorded yield, the platform summarises the air temperature, humidity, pH and EC that the higher-yielding records ran at, as conditions worth trying in the next recipe rather than proof of cause.

Price the light schedule

Target DLI and canopy PPFD set the hours the crop needs. The platform prices every half-hour start time and finds the lowest-cost window that still meets the target.

Assessment that reaches the model

Health scores, defects and stage quality recorded against the batch feed the daily features and trends that the prediction service reads.

Forecasts that say how they were made

Harvest-date forecasts use a trained model when data quality supports it and fall back to a cold-start estimate when it does not. Growth-stage estimates are currently rule-based. Both return confidence; harvest results identify cold-start estimates, while growth-stage results identify their method.

Orders beside capacity

Customer orders and available stock sit beside zone occupancy, so the team can see what is sold, what is growing and where capacity remains.

Cost before commitment

Capex, opex, energy and wastage modelling put a commercial figure beside an agronomic option before anyone signs it off.

What the models decide, and what they do not. Yield comparisons summarise historical records; they do not establish cause and effect or guarantee a better recipe. Harvest forecasting can use a trained model, while growth-stage estimates use rules and observations. Tariff comparisons calculate costs from the supplied rates and lighting assumptions. A person reviews these outputs before changing a recipe or schedule.

Next step

Test the forecasts against your own data

A 30-minute walkthrough against your zones, crops and sensors. No slides unless you ask for them.

  • Walkthrough led by someone who has built the system
  • We map your zones, crops and sensors before the call
  • Straight answers on what fits and what does not