Azoma Raises $11.2M: The London Startup Helping Brands Win Over AI Shopping Assistants

The next big shift in online shopping might not be about convincing you to click. It might be about convincing an AI to click on your behalf. London-based startup Azoma just raised $11.2 million in Series A funding to build exactly that: a platform that helps brands get seen, picked, and recommended by AI shopping agents.

According to reports published this week, the round was led by Puma Growth Partners, with participation from dunnhumby, Twin Path, MaRS IAF, Rank Ventures, Ignite Ventures, and angel investors from Google DeepMind and Legora. The raise brings the company’s total funding to $15.2 million. It lands at a moment when e-commerce brands are starting to ask an uncomfortable question: what happens to our sales when the customer is no longer a person browsing a website, but a software agent doing the browsing for them?

The raise: who is backing Azoma

Azoma is a London and Toronto based company led by CEO and co-founder Max Sinclair and co-founder Timur Luguev. The $11.2 million Series A was led by Puma Growth Partners. The list of participants is arguably more interesting than the headline number.

Dunnhumby is the retail data science company behind loyalty programmes like Tesco Clubcard, so its presence signals confidence that Azoma’s approach is grounded in real retail mechanics rather than AI hype. MaRS IAF, the investment arm of Toronto’s MaRS innovation hub, reflects the company’s Canadian roots. And the angel investors from Google DeepMind and Legora suggest the technical pedigree of the idea passed the smell test of people who work at the frontier of AI.

The new capital will go toward expanding the platform across AI personal assistants and growing the team: building for a world where shopping agents are everywhere.

What “agentic commerce optimization” actually means

Azoma describes its product as an agentic commerce optimization platform. The jargon deserves unpacking, because it points at a genuinely new discipline.

Traditional e-commerce marketing optimizes for humans: search rankings, social ads, influencer reviews, slick product pages. Agentic commerce optimization does the same job but for machine shoppers. When you ask an AI assistant to find running shoes under a set price, the assistant reads product data feeds, parses specifications, compares reviews across sources, and then recommends or buys one. Azoma’s platform works across product data, content, and third-party sources, combining measurement, optimization, and execution so that a brand’s products surface in those AI-driven recommendations. The function this describes is already becoming a line item in marketing budgets.

Why brands are suddenly worried about AI shoppers

It is easy to dismiss agentic commerce as a niche experiment, but the trend lines are hard to ignore. Every major AI company is racing to give its assistant the ability to act: browsing the web, comparing prices, filling carts, and completing purchases. For brands, this creates a strange new dynamic. Your product page can be beautiful, but if the AI agent reads only your structured product feed and finds it incomplete or inconsistent, you lose the sale without a human ever seeing your page.

There is also a subtler risk. Human shoppers are unpredictable in a way that favours variety: someone might buy the underdog brand because of a nice photo or a funny review. AI agents optimize ruthlessly against criteria like price, ratings, availability, and shipping speed. Brands that used to compete on storytelling and design now have to compete on structured data quality. That is an unfamiliar skill set for most marketing teams, which is exactly the gap Azoma is trying to fill.

Digital twins of shoppers: how the tech works

One of the more distinctive parts of Azoma’s pitch is its use of Digital Twin technology to simulate AI shopper personas. In plain language, the company creates virtual models of how AI shopping agents behave, then tests how a brand’s products perform inside those simulations.

This matters because AI agents are not one thing. The shopping agent built into one assistant may weigh reviews differently from another, or follow different protocols for fetching product data. Azoma says its platform covers agentic commerce protocols, the emerging standards that govern how agents discover, compare, and transact with merchants. By simulating multiple shopper personas, brands can see where their products disappear from recommendation lists and fix the underlying data problems before they cost real sales. It is the same logic that manufacturers have used for years with digital twins of factories, now applied to the sales funnel.

The bigger picture: a British bet on the agent economy

There is a distinctly British angle to this story. The United Kingdom has spent the past decade trying to convert its world-class AI research into homegrown commercial winners, with mixed results. London’s AI startup scene is strong in research talent but has historically watched value migrate to American giants.

Azoma fits the profile of the kind of company the UK wants to keep: technically ambitious, building on a platform shift early, and selling into a global market. The involvement of dunnhumby, a British data science success story, adds a nice symmetry. Whether Azoma becomes the defining name in agentic commerce or gets absorbed into a larger platform, the funding round is another data point in London’s claim to be Europe’s AI capital.

The agent economy is still taking shape, and plenty of today’s predictions will look naive in five years. But one thing already looks certain: brands will soon be selling to machines as well as people. The startups that help them do it are raising real money, right now.

Frequently Asked Questions

What is agentic commerce?

Agentic commerce refers to shopping transactions that are carried out, or heavily shaped, by AI agents acting on behalf of customers. Instead of a person browsing product pages, an AI assistant searches, compares, and sometimes completes purchases according to the buyer’s instructions and preferences.

What does Azoma’s platform actually do for brands?

According to the company, the platform combines measurement, optimization, and execution across product data, content, and third-party sources. The goal is to increase how often a brand’s products appear and get recommended in AI shopping experiences, using simulations of AI shopper personas to test and improve performance.

Who invested in Azoma’s Series A round?

The $11.2 million round was led by Puma Growth Partners, with participation from dunnhumby, Twin Path, MaRS IAF, Rank Ventures, Ignite Ventures, and angel investors from Google DeepMind and Legora. The round brings Azoma’s total funding to a reported $15.2 million.