How to make your e-commerce product visible to AI agents? Use this new system trusted by L’Oréal, Unilever, Mars & Beiersdorf

For future-focused e-commerce brands, the primary customer is rapidly changing from a person behind a screen to the AI agents that said human customer deploys on their behalf to research and, if projections are correct, purchase the product on their behalf.

Investment banking and financial services giant Morgan Stanley, for instance, has published research suggesting 10-20% of the entire U.S. commerce spend could be agentic by 2030 — amounting to $190 billion to $385 billion.

In response to this seismic shift, the four-year-old agentic AI e-commerce startup Azoma has unveiled the Agentic Merchant Protocol (AMP).

This new framework is designed to provide high-volume retailers — such as grocery brands, electronics manufacturers, and fashion labels — with a “brand-friendly” anchor in an ecosystem increasingly dominated by autonomous shoppers

The idea is compelling and deceptively simple at its core: instead of the current status quo wherein merchants selling physical products online have to manually enter information about each product like SKUs and materials on different online marketplaces and product listing aggregators (e.g. Walmart, Amazon, Google Shopping, etc.) — brands can now just take all that information, put it into Azoma’s platform, and push it out everywhere it needs to go, including pages optimized for AI agents to search and retrieve the information for users, recommending them the products that fit their specific query.

Using technology to end the ‘black box’ era of early agentic AI e-commerce

Modern AI integration typically relies on siloed systems like OpenAI’s ACP or Google’s UCP. While these protocols manage the technical handshakes required for discovery and payment, they offer minimal oversight regarding brand integrity.

When an AI agent deployed by a customer “reasons” about their human consumer’s product query, it often synthesizes data from unverified corners of the web, such as Reddit or outdated affiliate sites, creating a “black box” effect where the brand’s intended messaging is lost.

AMP functions as a high-level “system of record” that bridges these disparate platforms. It allows companies to centralize their product intelligence—including legal guardrails and brand books—into a single, machine-native format.

“AMP breaks the foundations of traditional ecommerce,” states Max Sinclair, CEO of Azoma in a press release shared with VentureBeat ahead of the official announcement set for March 12 in London.”For decades, marketplaces like Amazon and Walmart acted as gatekeepers by controlling product detail pages, rankings, and distribution. Brands optimized a finite set of endpoints: PDPs, ads, search results. In an agentic world, those fixed pages no longer exist”.

The Azoma platform is specifically engineered for high-volume retailers and manufacturers of physical goods, with a primary concentration on the Consumer Packaged Goods (CPG) and fast-moving consumer goods (FMCG) sectors.

In an interview with VentureBeat, Sinclair explicitly distinguished the protocol’s utility from digital-only assets or services, noting that Azoma does not currently support NFTs, SaaS, or financial sectors like banking and insurance.

Whether facilitating the automated reordering of household staples like dishwasher soap or providing the “reasoning” data for high-consideration purchases like specialized supplements and ski hardware, the protocol serves as the digital connective tissue for brands whose value is rooted in the physical world.

Sovereignty in a multi-agent world

The protocol has already seen rapid adoption by a coalition of consumer goods giants, including L’Oréal, Unilever, Mars, Beiersdorf, and Reckitt. For these organizations, maintaining a consistent identity across various AI surfaces is an urgent priority.

“The fact that businesses like L’Oréal, Unilever, Mars & Beiersdorf have moved so quickly to adopt AMP tells you everything about the urgency they feel,” Sinclair remarked during a recent interview with VentureBeat. “These are companies that have spent decades building brand equity—they’re not about to hand control of how their products are represented to an AI black box”.

The AMP suite provides several critical levers for technical leaders:

• Canonical Machine-Native Catalogues: Data structures designed specifically for LLM ingestion, enriched with persona-level signaling.

• Programmatic Open Web Distribution: Ensuring that the data agents find on the open web matches the brand’s official documentation.

• Agent-Agnostic Infrastructure: A design that prevents vendor lock-in by allowing brands to interface with any AI assistant or marketplace agent.

• Performance Visibility: Tools to measure how agents “weigh” specific product attributes and verify compliance across the ecosystem.

Intelligence as a competitive moat

Beyond simple data distribution, Azoma provides an end-to-end workflow designed to secure market share in an AI-first economy.

The platform includes a proprietary “RegGuard™ Compliance” engine that automatically audits all generated content against strict brand guidelines and regulatory rules, such as FDA/DSHEA standards.

This automated oversight is paired with advanced citation tracking, allowing brands to see exactly which sources—ranging from Reddit and Quora to Wikipedia and YouTube—AI agents are citing when they make a recommendation.

This granular visibility has already yielded significant performance gains for early partners. The company reports that for the brand Ruroc, site traffic from ChatGPT has increased 14x, positioning them as the #1 recommended ski helmet brand in target geographies.

Similarly, clients have seen their share of mentions within specific retail agents like Amazon Rufus increase by 5x, while optimized content has demonstrated conversion lifts of up to 32% in split-testing.

By addressing technical “GEO blockers”—such as schema errors, crawlability gaps, and JavaScript-only content that traditional scrapers might miss—Azoma enables brands to transition from passive observation to active optimization of the AI conversation.

For rapidly growing firms like Perfect Ted, this visibility contributed to a +532% year-over-year revenue increase.

Fusing marketplace DNA with AI research

Azoma’s leadership team mirrors the intersection of high-scale retail and advanced computation.

Sinclair spent six years at Amazon, where he spearheaded the customer browse experience for the Singapore launch and managed the expansion of Amazon Grocery throughout the European Union.

This tenure at the world’s largest retailer highlighted the limitations of static listings in a dynamic, AI-driven market. “In the traditional e-commerce world… you’d write a product listing, publish it, and that would be that,” Sinclair observed. “In this new world, the product detail pages are generative… our customers lose all of the control”.

The technical backbone of the protocol is led by CTO Timur Luguev, a Fulbright Scholar and ERCIM Fellow with over a decade in multimodal deep learning.

Luguev views AMP as a way to indirectly influence the broader “online footprint” that informs AI reasoning. “We want to feed agents through, basically, indirectly, through open online footprint,” Luguev explained.

“That’s the focus: basically first define this kind of a standard, so centralize this information about the product and the brand in one place, then syndicated across the open surfaces, and then quantify and measure the impact”.

Licensing and market implications

Azoma is positioning its protocol as a neutral alternative to the walled-garden approaches of major tech providers. While search engines prioritize the consumer’s user experience, AMP focuses exclusively on the merchant’s requirement for predictability and accuracy.

This shift effectively replaces traditional Search Engine Optimization (SEO) with Agentic Commerce Optimization (ACO).

Sinclair argues that this transition is driven by a shift in consumer trust. “You’re going to trust ChatGPT acting on over your data [more] than just putting into Google, ‘what mattress should I use’ and just clicking on whoever paid for that top link,” he says.

Pricing structure

Azoma’s commercial strategy is designed to bridge the gap between traditional enterprise software procurement and the performance-driven metrics of the AI era. Currently, the company utilizes a standard enterprise model, engaging with its global partners through annual contracts that typically fall within the six-to-seven-figure range. This structure is intended to align with the existing budgetary frameworks of large-scale organizations, providing the predictability required for multi-national department planning.

However, the company’s long-term vision involves a fundamental pivot toward an outcome-based pricing model. By integrating directly into a brand’s data and revenue flows, Azoma can measure the specific financial impact of every syndicated intervention across the agentic ecosystem.

“Our ambition is the future is kind of… taking a cut when they [agents] deliver value,” explained Sinclair.

This goal would effectively transition the protocol from a SaaS expense into a performance-based asset, mirroring how modern advertising platforms operate by tying costs directly to incremental revenue growth.

Outcome-based agentic e-commerce

As the market prepares for the official unveiling of the protocol at the Agentic Commerce Optimization event in London on March 12th, the message to the C-suite is clear: the “fixed” product page is dead. “When L’Oréal, Unilever and Mars move together in the same direction, the rest of the market pays attention,” Sinclair concluded.