An AI-driven climate tech startup that aims to help fashion brands and apparel companies reduce overproduction and improve inventory management through advanced AI technology has secured $ 500,000 pre-seed funding. The company, Nūl Global Technologies Pte. Ltd, is Singapore based.
Co-founded by two Indians, Malini Kannan and Raghav M.S., Nūl enables fashion companies to forecast demand more accurately, plan production effectively, and optimize inventory allocation.
By integrating AI with the brand’s existing operations, the startup transforms data into actionable insights that helps minimise waste.
The fashion industry accounts for up to 10 per cent of global carbon emissions. A single item of apparel can emit up to 6 kg of CO2, and approximately 30 per cent of fashion products go unsold, leading to waste through markdowns, recycling, or disposal. This loss accounts for 7-10 per cent of a brand’s revenue.
At the same time, brands often struggle with stockouts of bestsellers, missing out on potential revenue. Nūl said its technology seeks to balance these two challenges, reducing waste while maximising profitability.
“Overproduction is detrimental to both business and the environment,” said Malini Kannan, Co-founder and CEO of Nūl. “Traditionally, overproduction has been seen as an inevitable byproduct of legacy systems and long supply chains. We believe that next-generation technologies like ours can help fashion brands bridge these gaps significantly.”
Despite the size of the global fashion market—worth $2 trillion—few tailored solutions exist for inventory management that offer the depth of intelligence required to manage stock across size, style, colour, and location.
The startup addresses this gap by co-developing solutions with medium-sized fashion brands and keeping operational challenges at the forefront of its approach.
The startup’s platform allows brands to efficiently redistribute inventory, moving stock to locations where it is more likely to sell quickly, resulting in a 10% revenue uplift in these trials.
