Greyparrot launches Deepnest to unlock US$100B waste data opportunity

UK – Waste analytics pioneer Greyparrot has unveiled Deepnest, an AI-driven waste intelligence platform that gives brands unprecedented access to real-world data on the recyclability and end-of-life performance of their packaging.

As global regulations tighten and the circular economy gains momentum, Deepnest aims to fill a critical information gap — unlocking data from post-consumption waste streams that has long remained invisible to manufacturers and brand owners.

Deepnest is powered by Greyparrot’s proprietary Analyzer AI camera systems, currently deployed in material recovery facilities (MRFs) across more than 20 countries, where they analyze over 40 billion waste objects annually.

These AI-enabled cameras build the world’s most comprehensive household packaging waste database, detecting US$1 billion worth of recyclable materials in current waste streams.

Greyparrot estimates that, if deployed at scale globally, this technology could uncover up to US$100 billion in recyclable value by 2040.

By giving brands access to packaging performance insights segmented by brand, material, product type, and region, Deepnest eliminates guesswork and provides data-led, actionable recommendations.

Brands can now leverage advanced tools and analytics to significantly enhance the sustainability and effectiveness of their packaging strategies.

One of the key capabilities available is the ability to benchmark recyclability against competitors and prevailing industry standards.

This allows companies to understand where they stand in terms of packaging performance and make informed decisions to remain competitive in increasingly eco-conscious markets.

Additionally, brands can test packaging formats before committing to a full-scale rollout across their product portfolios.

This pre-launch assessment reduces risk and ensures that new packaging innovations meet regulatory requirements and consumer expectations around sustainability and functionality.

Another critical benefit is the ability to identify design flaws that may hinder recyclability in specific target markets.

By addressing these issues early in the development process, companies can avoid costly redesigns and ensure smoother integration into regional recycling infrastructures.

Finally, these tools enable brands to quantify the return on investment (ROI) from their R&D efforts, packaging innovations, and broader circularity strategies.

By measuring the environmental and economic impact of sustainable packaging decisions, businesses can justify continued investment and communicate their progress transparently to stakeholders.

“The term ‘waste’ is itself a misnomer, our data shows post-consumption materials are worth billions to the global economy,” said Ambarish Mitra, Co-founder of Greyparrot.

“Brands have been blind to what happens to their packaging after disposal. Deepnest changes that, offering them not just transparency, but a competitive edge.”

With extended producer responsibility (EPR) fees and virgin plastic taxes forecasted to cost global businesses up to US$100 billion annually by 2040, tools like Deepnest could help brands reduce financial risk, improve recyclability, and retain valuable materials within their own supply chains.

Unilever, Amcor, and Asahi Beverages are among the high-profile companies piloting Greyparrot’s technology.

Dr Liz Smith, Global R&D Head at Unilever, praised Deepnest for its “potential to provide new visibility into how packaging is sorted and recycled in practice.”

Sandra Gibbs of Asahi Beverages highlighted how the platform will support data-driven packaging lifecycle decisions, especially in maximizing output from the company’s PET recycling facility.

As the packaging sector seeks real-world impact beyond lab simulations and models, Deepnest offers a timely and transformative solution.

Greyparrot’s innovation not only enhances sustainability but sets a new standard for data transparency, compliance readiness, and circular design in the global packaging value chain.

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