For retailers, artificial intelligence is only as useful as the data it can read. A chatbot that cannot understand product attributes, stock availability, customer intent, or the difference between two near-identical SKUs is unlikely to move the needle on sales. That is the gap Singapore-based Graas is trying to close with its latest fundraise and acquisition.
Graas, a retail commerce AI company, has raised US$17 million in a Series B round and acquired Trustana, a Singapore-based product-data platform. The round was led by LemmaTree, an investment firm founded by Singapore state investor Temasek, with participation from Integra Partners, Tin Men Capital, The Xander Group, IncredWealth and Orzon.
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The financial terms of the Trustana acquisition were not disclosed.
Founded in 2022, Graas builds AI tools for retail commerce, drawing on more than US$1 billion in live commerce transactions processed through its platform.
Trustana, founded in 2020, helps retailers and distributors automate the enrichment and structuring of product information, the less glamorous but critical layer that determines whether a product can be discovered, compared, recommended, and sold accurately across channels.
Graas said Trustana’s product-data enrichment capabilities will be integrated into its Commerce Knowledge Graph, the underlying database that powers its AI agents for retail clients. In simple terms, a knowledge graph connects different pieces of information (products, customers, inventory, transactions, content, pricing, and behaviour) so that AI systems can make better decisions.
For a retailer, that could mean an AI agent which does not merely answer a customer’s question, but understands whether a product is in stock, whether a similar item has a higher margin, what the customer has bought before, and how to recommend the right option across chat, voice, or image-based search.
Why product data matters
The Trustana deal points to a wider shift in retail AI. Over the past year, much of the attention has gone to customer-facing generative AI tools: shopping assistants, automated ads, AI-written product descriptions, and conversational search. But these tools break down quickly when the underlying data is incomplete, inconsistent, or scattered across different systems.
This is a common problem in Southeast Asia, where retail is fragmented across online marketplaces, brand websites, social commerce, supermarkets, distributors, and general trade channels. A consumer may discover a product on TikTok, compare it on Shopee or Lazada, buy it through a brand site, and later repurchase it at a neighbourhood store. For large brands, stitching this journey together is difficult.
The challenge is even sharper for companies operating across multiple markets. Product catalogues are often maintained in different languages, formats, and systems. The same item can carry different descriptions, categories, pack sizes, and images depending on the sales channel. Without cleaning and structuring that data, AI agents risk producing poor recommendations or, worse, misleading answers.
This is where Trustana fits into Graas’ broader pitch. By adding Trustana’s product-data layer to customer and inventory information, Graas wants to build AI agents that can support both e-commerce and offline retail channels.
Prem Bhatia, co-founder and CEO of Graas, said bringing product, customer, and inventory data into a single system would allow AI agents to drive sales in both e-commerce and general trade.
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That focus on general trade is significant in Southeast Asia. Despite the rise of digital commerce, a large share of consumer goods sales in the region still happens through small shops, distributors, and offline channels. For global brands such as Unilever or Puma, AI tools that only optimise marketplace ads or online storefronts solve only part of the problem.
A broader customer base
The acquisition also gives Graas access to Trustana’s customer relationships. Trustana’s clients include David Jones, Chemist Warehouse, and Toys”R”Us. Graas already serves brands including Unilever, Puma, and Schneider Electric, with operations across Australia, Southeast Asia, and the Gulf.
This regional spread matters. Retailers in mature markets such as Australia may have more structured digital operations, while Southeast Asian markets tend to involve more fragmented distribution and marketplace-led commerce. The Gulf, meanwhile, has seen rising investment in retail digitisation, particularly among large consumer brands and mall-based retail groups.
For Graas, combining these markets could offer a wider base of commerce data and use cases. The company says its platform already draws on more than US$1 billion in live commerce transactions, a figure that gives its AI systems more context on how consumers browse, compare, and buy.
Rebecca Xing, CEO of Trustana, said the combination would help the company accelerate growth and deliver more to its global customers.
Glenn Gore, CEO of LemmaTree, said the combined platform is positioned to help retailers make use of agentic AI — a term used to describe AI systems that can take actions on behalf of users, rather than simply generate text or analysis.
The term is becoming common in enterprise software, though it remains loosely defined. In retail, an agentic AI system could monitor inventory, recommend pricing changes, generate product content, suggest campaign adjustments, or respond to customer queries with purchase options. The practical value depends on how well the system is connected to real-time business data.
The competitive field
Graas is operating in a crowded and fast-changing market. Globally, large software providers such as Salesforce, Adobe, Shopify, Bloomreach, Algolia, and Dynamic Yield are embedding AI deeper into commerce, personalisation, search, and customer engagement tools. In Asia, companies such as Insider also compete in customer experience and marketing automation, while marketplace operators and e-commerce enablers offer their own analytics and optimisation layers.
Graas’ differentiation appears to lie in its focus on retail commerce data across multiple channels, rather than only storefront software or marketing automation. The Trustana acquisition strengthens that positioning by adding product intelligence, a foundational layer that many AI commerce tools still depend on but do not always control directly.
Still, execution will be the test. Enterprise retail customers are often slow to replace core systems, and many already use a mix of ERP, CRM, marketplace, and analytics platforms. To win larger accounts, Graas will need to show that its AI agents can plug into existing workflows, improve sales or margins, and reduce the manual work that typically sits behind catalogue management and commerce operations.
Singapore’s AI commerce play
The deal also reflects Singapore’s continued role as a base for regional enterprise technology companies. The city-state’s proximity to Southeast Asian markets, access to capital, and concentration of regional headquarters make it a natural launchpad for companies selling to large brands across Asia-Pacific and the Middle East.
For investors, retail AI offers a large but demanding opportunity. Consumer brands are under pressure to grow across more channels while managing tighter margins and higher customer expectations. AI promises efficiency, but retailers are increasingly looking beyond experiments and pilots. They want systems that can produce measurable commercial outcomes.
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Graas’ fundraise and acquisition suggest a bet that the next phase of retail AI will not be won by flashy interfaces alone. It will depend on whether companies can organise messy commerce data well enough for AI agents to act on it reliably.
In a region where retail remains both deeply digital and stubbornly offline, that may be the harder problem — and the more valuable one to solve.
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