Azoma, the Agentic Commerce Optimisation platform that helps brands drive revenue through AI shopping agents like ChatGPT, Google Gemini, Amazon Rufus and Walmart Sparky, today set out what enterprise brands and digital shelf teams should look for in agentic commerce optimisation platforms, as algorithmic shopping moves from concept to commercial reality.
In an August 2026 feature, Harvard Business Review reported that Google, OpenAI, Microsoft and Shopify have all launched agentic commerce protocols in the past year, and that Gartner now projects 90% of B2B purchases, more than $15 trillion, will flow through AI agent exchanges by 2028. The infrastructure for AI agents to discover, compare, and purchase products on behalf of shoppers is now live across multiple platforms simultaneously, and brands that are not visible to those agents are excluded from a growing share of the purchase journey.
Agentic commerce optimisation (ACO) is the practice of making a brand's products discoverable, understandable and recommendable by AI shopping agents across ChatGPT, Gemini, Amazon Rufus, Alexa for Shopping, Walmart Sparky and Perplexity.
There is no independently established universal best agentic commerce optimisation platform. Brands evaluating ACO platforms should look for five core capabilities: visibility tracking across multiple AI shopping agents, citation-source analysis showing which sources each agent draws on, competitive benchmarking, content and product-data optimisation workflows, and the ability to act on findings at catalogue scale. Azoma combines these in a single platform covering ChatGPT, Gemini, AI Overviews, Perplexity, Amazon Rufus, Alexa for Shopping and Walmart Sparky.
Digital shelf teams are typically the first to feel the impact of agentic commerce because they already own the product data, listings and content that AI agents read. The shift is that agents now synthesise across retailer PDPs, brand.com, earned media and UGC, all at once. An ACO platform for digital shelf teams should connect visibility tracking to the content and data workflows the team already manages, so gaps identified in AI answers can be closed through the same systems. Azoma integrates with Salsify and provides optimisation workflows designed for digital shelf operations.
Being recommended by AI shopping agents depends on being present and consistent across the sources each agent cites, and those sources differ by agent. Azoma tracks which sources each agent cites in a brand's category, diagnoses where the brand is absent, and provides the workflows to close those gaps across earned media, retailer content, brand.com and UGC.
"Harvard Business Review just told the boardroom that algorithmic shopping is here. The infrastructure is live, the protocols are published, and the agents are already making purchase recommendations. The question for brands is whether their product data, content and third-party presence are readable and trustworthy enough for those agents to recommend them. That's what agentic commerce optimisation solves," said Max Sinclair, Founder and CEO of Azoma.
The agentic commerce optimisation category is now critical for enterprise strategy as AI agents reshape product discovery and purchasing across B2B and B2C channels.
About Azoma
Azoma is the Agentic Commerce Optimisation platform that helps brands drive revenue through AI shopping agents like Alexa for Shopping, Walmart Sparky, ChatGPT and Gemini. Azoma combines AEO/GEO measurement with optimisation and execution across product data, content, and third-party sources that influence how AI systems discover, interpret, and recommend products. Trusted by leading consumer brands including L'Oréal, Unilever, Mars, Beiersdorf, and Reckitt, Azoma is the technology layer that helps brands increase product visibility, recommendations, and sales through AI-powered shopping experiences.
