Nature Technology

Custom Tools & Integrations

Custom AI models, data pipelines and integrations that extend NatureCube for your programme.

Overview

Custom tools extend NatureCube where a programme needs something it doesn't do out of the box: an AI model trained for your species or sensors, a data pipeline from your field devices, or an integration with your corporate systems. AI in particular is useful when datasets are too large or too repetitive for manual review alone, and the model has to be built around the question and the data you hold, and scientists have to be able to check the output before it is treated as evidence.

Custom Tools & Integrations

How we approach it

We start with the decision the model is meant to speed up: species labels on camera traps, calls in a recording, or a class on a satellite tile. Training, testing and error rates are documented. The workflow includes a review step so an ecologist can correct the machine and those corrections can improve the next run.

What we deliver

  • Problem framing against the ecological question, not a generic model
  • Model development and evaluation on your imagery, audio or spatial data
  • A review workflow so experts can accept, reject and correct automated results
  • Documentation of performance, failure cases and how results should be used
  • Data pipelines and integrations with field devices, sensors and corporate systems
  • Advice on what should live in NatureCube and what should stay in other tools
  • Bespoke sensor development where off-the-shelf hardware doesn't fit
  • Testing against a real batch of your data before go-live
  • A handover so the model is not a black box only one person understands
NatureCube review screen showing a bird call spectrogram with its AI label and confidence score

What you receive

You receive a working analysis path, a plain statement of how well it performs, and a way for your scientists to stay in control of the result.

When you need it

Use this when cameras, acoustics or other sensors are producing more data than a team can review, and the result still has to be scientifically defensible.