AI waste analytics for MRFs and PRFs

You can’t optimise if you don’t measure

A lack of waste composition data means that operators aren’t getting an accurate look at their waste streams. Here’s why that’s a problem:


1. No control of material quality

Poor visibility risks poor quality. Without knowing the composition of waste streams, it’s difficult to guarantee the purity of final materials.


2. Reliance on manual labour

Manual labour is scarce, inefficient and costly. Over-reliance on manual sampling or sorting introduces safety risks and human error.


3. Inefficient sorting processes

Current sorting methods rely on single sensors. They don’t have the capacity to characterise materials at scale, increasing the reliance on manual pickers.


Our solution to the visibility challenge


Why automate waste composition analysis?

With real-time composition data, operators can continuously monitor waste streams. That data makes it possible to improve plant efficiency, automate manual sampling and verify bale quality to avoid returns.


Save up to £200k every year with automated sampling.


Save £300k a year by recovering just 5% of valuable materials otherwise lost to residue.


Save over £100k a year on manual sorting with the help of robotics.

More applications

What our customers say

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Semi-automated sampling for MRFs

Analyse infeed materials from individual suppliers with our AI-powered sampling cabin system.



For reprocessors and recyclers

Maximise profit margins from your infeed materials with continuous and automated composition analysis.



For waste traders

Guarantee material grade and purity certification in real time to build trust with suppliers and customers.


For plant equipment manufacturers

Empower your sorting machines with AI. Our open API can integrate with robots, optical sorters, balers, and PLC systems.


For plant builders

Design, model, test and build waste processing facilities based on accurate composition data.


For producers and regulators

Guide recyclable packaging design and inform policies like EPR with previously unavailable insights.


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