Greyparrot has raised €23 million (reported as $27 million) in a Series B funding round led by investor Omar Mir to accelerate deployment of its artificial intelligence systems for waste sorting, the company has announced. The investment follows claims that the company’s image-based AI has now detected more than one trillion waste objects and is installed above sorting belts in recycling facilities in over 20 countries.
What the funding will target
The company says the capital will support rapid expansion across North America and Europe and bolster its AI, data science and product teams. Greyparrot’s camera systems, branded as Analyzers, aim to identify materials, products and brands in real time and deliver continuous data on the composition and movement of waste through sorting facilities.
Operational claims and real-world benefits
Greyparrot reports that operators using its real-time data have seen efficiency improvements of between 10% and 30%, with one site cited as saving more than £1.5 million in a single year. The company estimates that materials identified by its network amount to a recoverable value of roughly £1.87 billion, including around 17.4 billion PET bottles and 4.2 billion aluminium items.
Customers named by the company include major waste and recycling groups such as Veolia, Biffa and FCC, which are reported to use the system to improve recovery rates, monitor material quality and increase facility profitability. Greyparrot also highlights that continuous visibility helps operators strengthen audits and compliance reporting and plan for regulatory-driven changes to waste streams.
“This funding lets us scale rapidly across North America and Europe and grow our AI, data science and product teams. The technology, market demand, urgency and momentum are all here, and we are ready to scale our impact.”
The quote comes from Greyparrot’s co‑founder and chief executive, reflecting the company’s intention to leverage the new capital to grow its footprint and capabilities.
Regulatory milestone
In what Greyparrot describes as a reported “UK first”, the Environment Agency accepted waste composition data derived from the company’s AI for statutory compliance reporting in the first quarter of 2026, in submissions from customers Biffa and FCC. That acceptance is likely to be seen as a significant precedent for use of machine-generated data in regulatory processes.
Context and consequences
The announcement sits at the intersection of several trends: increasing regulatory pressure to recover and recycle materials, the commercialisation of computer vision and embedded analytics, and investor appetite for climate‑tech solutions that promise measurable operational savings. If Greyparrot’s performance claims withstand scrutiny at scale, real‑time material identification could change how sorting facilities operate and how regulators verify compliance.
However, independent validation of automated composition data, long‑term robustness of detection in diverse facility environments, and integration with existing sorting hardware and contracts remain practical challenges. The Environment Agency’s acceptance for specific submissions does not automatically certify the technology for all use cases or operators, but it does open the door for wider consideration of AI‑derived evidence in the sector.
Quick facts
- Funding: €23 million (reported as $27 million) Series B, led by Omar Mir
- Deployment: Analyzers installed in facilities across more than 20 countries
- Scale metrics: >1 trillion detected waste objects; estimated recoverable value ~£1.87 billion
Greyparrot has also recently worked with corporate partners to validate packaging performance at commercial scale. The company’s public materials emphasise both the environmental and commercial value of improving recovery rates – a proposition likely to appeal to large waste processors and consumer goods companies facing tighter regulation and extended producer responsibility schemes.
| Metric | Reported figure |
|---|---|
| Series B funding | €23 million ($27 million) |
| Detected objects | More than 1 trillion |
| Countries with installations | Over 20 |
| Estimated recoverable value | £1.87 billion |
As the company scales, observers will be watching for independent assessments of detection accuracy across material types, longevity of hardware in harsh environments, and the commercial terms that tie AI insights to material recovery so the stated environmental benefits translate into verified reductions in landfill and incineration.
