Cosmetics retailer Mecca has partnered with packaging technology company Phantm to use artificial intelligence (AI) to collect, analyse and report packaging data across its operations.
The partnership will see Mecca build a packaging data asset using Phantm’s image-assisted AI platform, which converts images, PDFs and supplier specifications into a structured database. The technology provides insights into packaging materials, formats and weights, helping teams identify opportunities to reduce cost, waste and emissions while improving packaging reporting.
“Reliable packaging data is fast becoming a competitive advantage,” said Elliot Costello, co-founder and CEO, Phantm. “For Mecca, this means faster insights, lower costs and stronger foundations for future reporting and sustainability performance.”
Ricardo Pinto, sustainability manager at Mecca, said, “Phantm is helping us build a structured packaging data asset that not only reduces complexity in an uncertain regulatory landscape but also accelerates packaging innovation across our portfolio. After evaluating multiple solutions, Phantm emerged as the ideal partner.”
The move comes as Australian brands face increasing pressure to improve packaging sustainability and transparency. Experts estimate only around 14 per cent of plastic packaging is recovered for recycling in Australia, and extended producer responsibility frameworks are placing greater accountability on companies. Scope 3 reporting also expands responsibility across a company’s value chain, with packaging representing a key element of these emissions.
Costello said AI allows brands to access accurate packaging data at scale, reducing the time to gain insights from months to minutes. While the initial focus is on Mecca's packaging operations, he noted a broader trend in FMCG, beauty and retail sectors towards automation and data intelligence.
Phantm’s tools are already in use by major FMCG, food and beverage businesses, and beauty brands, providing visibility into packaging performance, design efficiency and material recovery potential.
