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Cultivation technology · New Zealand

Sensors, data and apps for controlled-environment growing.

GrowLabs builds the tools growers use every day: sensors that read the room, apps that keep the records, and Bud, an AI assistant that helps your team get the work done.

See our apps

Growing creates data. The hard part is acting on it.

A grow room runs on hundreds of small decisions: climate adjustments, nutrient checks, crop inspection, pruning, harvest timing and stock counts. Many systems record this information. Few read plant, environmental and operational signals together, and fewer turn them into the next job on someone's list.

GrowLabs works on that gap. Capture the right data. Make sense of it. Help people act on it.

Meet Bud, the assistant inside AiGrow

AiGrow is our cultivation operations app. Bud is the AI assistant built into it. Ask Bud in plain language, by text or voice, and it helps you:

  • Complete tasks. Find what is due, record what was done, and keep the work moving.
  • Run operations. Plants, batches, rooms, recipes and harvests in one place.
  • Track inventory. Stock levels and counts, kept with the rest of your records.

AiGrow is in development. See AiGrow

What we work on

Three parts, built to work together.

Our apps

Each one does a single job well. Together they cover the grow.

Who it is for

Growers who run controlled environments and want their data to do more.

  • Medicinal cannabis growers

    Growers who need tight records, consistent crops and clear reporting.

  • Other controlled-environment growers

    Indoor and hydroponic operations growing high-value crops.

  • Research partners

    Teams who want to test sensing, data and guidance in real grow rooms.

From data to decisions

  1. 1

    Capture

    Collect environmental readings, plant images and operator inputs from the grow room.

  2. 2

    Interpret

    Train models to tell normal variation apart from stress, deficiency, disease risk or reduced performance.

  3. 3

    Recommend

    Turn findings into timely guidance and tasks, tested against human observation and crop outcomes.

  4. 4

    Improve

    Use each cultivation cycle to sharpen data quality, model accuracy and ease of use.

A staged R&D programme

  1. 1Stage 1

    Discovery and baselines

    Sensor testing, data capture, literature review, first software architecture and cultivation baselines.

  2. 2Stage 2

    Experiments and models

    Machine learning and computer vision development, recommendation experiments and operator comparisons.

  3. 3Stage 3

    Validation and integration

    Repeated grow cycles, model refinement, recommendation validation and system integration.

Where we're heading

Today we build sensors, data tools and apps. Next, Bud's guidance moves onto the grow-room floor through augmented reality. Further out, robots that you direct from GrowLabs apps. That is a direction, not a product yet.

Read where we're heading

Open cultivation science

We publish the Cannabis White Papers: 55 open papers on cultivation, from propagation to harvest. Each fact carries a citation. Free to read and share under CC BY-NC.

Working on controlled-environment growing?

Tell us about your grow, your data or your research. We reply from New Zealand.

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