Amazon Agentic Commerce: Alexa, COSMO, and What’s Next

As of May 13, 2026 Amazon has retired the Rufus brand name and has integrated Rufus’ tech into their new shopping agent: Alexa for Shopping.

The Skinny

Amazon agentic commerce is here, but it’s uneven. Amazon is playing defense while it quietly turns Alexa for Shopping and COSMO into first-class commerce surfaces. At Tinuiti, we’re betting on a Alexa for Shopping-centric near term, where the brands that win are the ones restructuring PDPs for agents, not just humans.

Alexa for Shopping is already agentic commerce at scale on Amazon, but today it’s a discovery and comparison layer, not a fully transactable checkout surface.
COSMO, Amazon’s e-commerce knowledge graph, rewards PDPs that front-load outcomes, use cases, and constraints instead of feature-stuffed copy.
Amazon’s blocking of many AI crawlers, its lawsuit against Perplexity, and its continued dominance in AI citations, where Googlebot is allowed, all point to a strategy of keeping agentic behavior within its own walls.
McKinsey projects that AI agents could mediate trillions in global commerce by 2030, but our view is long-run bull, short-run bear—especially given current UX, trust, and measurement gaps.
Our 2026 playbook for brands focuses on three things: Alexa for Shopping-ready PDPs, AI visibility (citations and mentions), and a measurement stack built on AMC, MMM, and incrementality instead of last-click alone.

Amazon Agentic Commerce: Alexa for shopping, COSMO, and What’s Next

Agentic commerce on Amazon is reshaping how shoppers discover, evaluate, and buy products today. At Tinuiti, we see this shift first-hand in client data, through our partnership with Profound, and in how Amazon itself is restructuring its retail media surfaces around Alexa for Shopping and COSMO.

We also see the tension. Agentic commerce isn’t one thing; it’s a spectrum. The term “agentic commerce” isn’t just analyst hype. McKinsey’s latest work on agentic commerce argues that AI-mediated shopping could rival the impact of the web and mobile waves by 2030, both in volume and in the structure of the customer journey.

Amazon is playing defense, not offense. Alexa for Shopping is Amazon’s agentic beachhead. COSMO is the content layer brands are still underestimating. Our job as a media agency that architects business outcomes, not just campaigns, is to help brands turn that complexity into measurable growth while ending waste.

What Agentic Commerce Actually Means for Amazon Brands

The Spectrum of Autonomy (and Where Amazon Shoppers Actually are)

In our work across AI search and commerce, we define agentic commerce as a spectrum of autonomy, not a binary on/off switch. For Amazon brands, three levels matter:

Comparison shopping: AI helps weigh options, and the human makes all final decisions.
Buy-for-me: rule-based autonomy (price tracking, conditional purchase, budget caps).
Full autonomous transaction: delegated credit card and judgment (“plan a party for 25 and handle everything”).

Most real behavior we see on Amazon today still sits in that first tier. Alexa for Shopping behaves as a discovery and comparison agent layered on top of search, not yet as an end-to-end transaction agent. According to our 2026 AI Trends Study, which surveyed 1,037 US adults, 48% of consumers (nearly half) indicated they would trust an AI assistant or chatbot to recommend products for them. However, only 20% (exactly one in five) said they would trust AI to actually purchase products or complete orders on their behalf.

That gap between “help me shop” and “shop for me” is where Amazon brands have to operate in 2026. When we talk about “Amazon agentic commerce,” we’re not talking about some sci-fi future where bots own every purchase. We’re talking about any interaction where an AI layer meaningfully shapes which products a shopper considers, evaluates, or buys on Amazon. Under that operational definition, Alexa for Shopping is already agentic commerce at scale.

“Agentic commerce isn’t a binary on-off switch. Instead, it encompasses any experience where AI assistance is meaningfully shaping the journey.”
Simon Poulton, EVP Innovation & Growth

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Why Definitions Matter for Amazon Advertisers Specifically

When marketers argue about whether agentic commerce is “real,” they’re often working from different definitions and therefore talking past each other. For us, and for the brands we support, the only useful definition is one that changes how we allocate budgets, structure content, and measure incrementality.

On Amazon, that means focusing less on whether a transaction technically occurs within a chat interface and more on where influence occurs. Our AI in Search research with Profound shows that AI answers are increasingly where consideration happens, even when the final click happens elsewhere. Alexa for Shopping sits squarely in that decision layer for Amazon. If an AI assistant is narrowing the shortlist your brand appears on—or doesn’t—that is agentic commerce, and it has direct implications for retail media performance, PDP structure, and how you think about branded vs. non-branded demand.

For Amazon advertisers, clarity on that definition is non-negotiable. It’s the difference between treating Alexa for Shopping as a novelty widget and treating it as a strategic surface that will influence your Total Advertising Cost of Sales (TACoS)*, your long-term ROAS, and your ability to defend brand search over the next 12–24 months.

*TACoS is a Tinuiti metric measuring total ad spend relative to total revenue (organic + paid), rather than just ad-attributed revenue. It offers a comprehensive view of business health, helping identify if ad spend is fueling growth or merely replacing organic sales.

Amazon’s Strategic Posture on Agentic Commerce

The Alexa for Shopping Revenue Signal, and What it Tells us

Amazon has started explicitly calling out Alexa for Shopping in earnings conversations, positioning it as a meaningful contributor to engagement and revenue rather than a side experiment. Even setting aside any specific numbers that get floated in commentary, the bigger signal is that Amazon is willing to put Alexa for Shopping on the investor scorecard. When a capability gets that level of attention, it’s a clear indicator to us that it has graduated from “test” to “surface.”

In parallel, Alexa for Shopping now serves sponsored placements in beta. That means two important things for advertisers:

Alexa for Shopping is already integrated into the monetization engine that underpins Amazon’s retail media business.
Even though you can’t bid on prompts directly yet, you can already see Alexa for Shopping prompt data inside the reporting layer, giving you an early view into how conversational queries differ from traditional keyword queries.

For a media agency responsible for both brand and performance outcomes, those prompt patterns are a new kind of intent signal we can’t afford to ignore.

Why Amazon Is Being Defensive: The Perplexity Lawsuit as a Tell

If you zoom out to the broader AI ecosystem, Amazon’s behavior around external agents looks increasingly defensive. In late 2025, Amazon sued Perplexity over alleged attempts to circumvent Amazon’s bot restrictions in order to power an agentic shopping experience. By early 2026, Amazon’s robots.txt file was blocking nearly 50 AI-related user agents, including all three primary OpenAI crawlers. At the same time, eBay has explicitly banned agents from transacting on its platform.

The honest read is that agentic commerce disintermediates retail media. When a consumer buys through an external agent, Amazon doesn’t get a chance to serve an ad. Yet, retail media has been the fastest-growing part of Amazon’s advertising business in recent years, as we see across our Digital Ads Benchmark Reports and client portfolios. Contrast that with Walmart, which has leaned into partnerships with OpenAI and others. Walmart has less entrenched retail media economics to protect, so it’s more willing to let external agents sit between its surfaces and the shopper.

This divergence is a major reason we track AI citation trends across platforms. In our Q1 2026 AI Citation Trends Report, Amazon.com remains the most commonly cited ecommerce site across the top generative AI platforms overall—but its share is much higher on Google AI Mode and AI Overviews, which rely on Googlebot, than on ChatGPT, which is governed by the Google-Extended and OpenAI crawlers Amazon blocks. Amazon wants humans to come to Amazon, use Alexa for Shopping there, and keep agentic behavior inside its own walls.

What Amazon’s Strategy Likely Looks Like From Here

Taken together, the signals point toward a consistent strategy. Amazon invented the “Buy Now” button, and it is almost certainly going to be the company that brings full transactability into Alexa for Shopping , inside its walled garden, rather than letting external agents broker Amazon purchases. From our vantage point, that’s not just a product bet; it’s a defensive moat around a multi-billion-dollar retail media business.

For brands, the implication is straightforward: the near-term future of Amazon agentic commerce is Alexa for Shopping-centric, not cross-platform. We recommend optimizing for Alexa for Shopping first, and treating off-Amazon agentic protocols as opportunistic extensions rather than the core of your Amazon strategy. As Amazon tightens access for external crawlers while still dominating AI citations wherever Googlebot is allowed, the brands that treat Amazon’s own agent surfaces as primary will be better positioned than those waiting for a single universal shopping agent that may never fully materialize.

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Alexa for Shopping as Amazon’s Agentic Commerce Engine

What Alexa for Shopping is Doing Today (And What it Can’t do Yet)

Right now, Alexa for Shopping sits atop the traditional Amazon experience as an embedded assistant that helps shoppers discover products, compare options, and get post-purchase answers. It does not yet complete transactions directly inside the chat surface. That matters because it means Amazon can frame Alexa for Shopping as an enhancement to its existing funnel rather than a replacement for it.

From a media and measurement standpoint, the important development is the introduction of sponsored placements inside Alexa for Shopping. Advertisers can already see Alexa for Shopping prompt data and associated ad performance in reports, even though there is currently no way to bid on specific prompts. We’re treating that prompt layer as a new research surface, one that reveals intent themes we never would have seen in a traditional search query report, and that can inform both PDP content and Sponsored Products/Sponsored Brands strategy in parallel.

How Alexa for Shopping Changes Discovery: Branded, Longtail, Intent-Driven

One of the clearest shifts we see in client data is that shoppers arrive at Alexa for Shopping with more specific needs than they bring to a standard search bar. They’ve often already done some of the research elsewhere and come into Amazon looking to validate a direction or finalize a shortlist, not to start from scratch.

As a result, generic category head terms lose relative share, while branded and longtail queries gain. Alexa for Shopping rewards products that can answer “who-when-why-with-what” questions in context: who is this for, when should I use it, why is it better than alternatives, and what is it compatible with. That is a fundamentally different content requirement than the keyword-stuffed, feature-first PDPs that were designed for an era of pure text search. In our AI in Search work, we see the same pattern across AI Overviews and chat interfaces: LLMs favor content that maps cleanly to intent and scenarios, not just to queries.

When Alexa for Shopping Becomes Transactable

We don’t see a credible path in which Amazon chooses to expose full transactability primarily through third-party protocols such as Google’s Universal Commerce Protocol (UCP) or OpenAI’s Agentic Commerce Protocol (ACP). The more likely scenario is that Amazon brings “Buy Now” directly into Alexa for Shopping and treats external protocols as edge cases or controlled partnerships.

When that happens, there are a few early signals we’re watching on behalf of clients:

Whether Alexa for Shopping ad placements become a distinct campaign type vs. remaining nested under Sponsored Products/Sponsored Brands.
Whether bidding is structured around contextual prompts, prompt templates, or still at the keyword level.
How the Buy with Prime API evolves as a potential rail for off-Amazon agentic checkout for DTC-heavy brands, without becoming the primary agentic strategy.

Our point of view, based on current product behavior and Amazon’s history, is that this is a “when,” not an “if.” Brands that wait for the full transaction switch to flip before restructuring content and measurement will be at least a cycle behind those already optimizing for Alexa for Shopping discovery.

COSMO and the Content Layer Most Brands Are Missing

What COSMO Is in Plain English

Under the hood, Alexa for Shopping depends on COSMO, Amazon’s e-commerce common sense knowledge graph. COSMO is a system that maps products to scenarios, needs, use cases, and shopper constraints. In Amazon’s own research, COSMO is framed as the infrastructure that enables the system to distinguish between a “lunchbox for a kindergartner who bikes to school in the rain” and “meal prep containers for office lunches,” even if both fall under similar catalog categories.

When Alexa for Shopping answers a conversational query like “what’s the best setup for a small apartment home gym,” COSMO enables it to infer the constraints, surface the right mix of products, and explain why they fit the scenario. From our perspective, that means every PDP, image, and piece of supporting content is effectively training data for COSMO, and by extension, for whatever agentic experiences Amazon builds next.

Why LLM-Structured Content Outperforms Human-Structured Content

In partnership with Profound, we’ve seen that many LLMs behave like extremely fast, somewhat lazy skim readers. One internal analysis suggests that a disproportionate share of citations tend to come from the first third of a piece of content, not the full page. That tracks with what we see in AI search: models lock onto early signals and then discount repetitive or shallow information further down the page.

Most Amazon PDPs were never designed for that reality. They lead with feature-first titles, marketing-led bullet copy, and boilerplate A+ content that speaks to human scanners but offers little structured, scenario-based context for machines. We consistently see better AI visibility and inclusion when content is structured for LLM ingestion: clear headings, outcome-led bullets, explicit use cases, and constraints spelled out in machine-readable ways.

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How to Restructure Content for Alexa for Shopping and COSMO: A Practical Checklist

For our Amazon clients, we’ve distilled the COSMO-ready content play into a simple PDP checklist:

Front-load the meat. Lead each bullet with the functional outcome, not the feature name. “Keeps lunches cold for 12 hours” beats “Insulated double-wall construction.”
Write for use cases, not just categories. COSMO indexes scenarios like “for hiking,” “for baby’s first solids,” or “for dorm rooms.” Make those phrases explicit in bullets and A+ modules.
Answer who-when-why-with-what. Who is it for, when should they use it, why is it better, and what is it compatible with? Alexa for Shopping leans heavily on that context for comparisons.
Include constraints. Size, weight capacity, material, temperature ranges, allergens, compatibility specs—these are the machine-readable details that agents use to filter.
Update the top three PDP images. Amazon’s top image slots are increasingly being indexed by LLMs, which means those visuals now do double duty: they need to be compelling for humans and legible for agents.
Treat A+ content as secondary discovery, not a brochure. Use it to answer comparison questions and scenarios that don’t fit naturally into bullets, not just to restate features.

“LLMs get really lazy when they read through the content. Just like a human, if someone starts explaining a concept to me and I halfway through go, ‘yeah, I know, I already know it,’ I’ll be like, ‘yeah, yeah, I got it, I got it.’ So I’m not really even listening to the rest of what they’ve said.”
Simon Poulton, EVP Innovation & Growth

What Amazon Agentic Commerce Looks Like Right Now

Three Patterns Brands Should Expect to See

Across categories, we’re seeing three recurring patterns in how shoppers mix AI tools and Amazon:

Off-Amazon discovery → Amazon purchase. Shoppers research on ChatGPT, Perplexity, Gemini, Reddit, or TikTok, then arrive at Amazon already leaning toward a specific brand or product. In Simon’s read, only a very small share of AI-influenced journeys actually complete on the AI platform itself; most still close in a traditional ecommerce environment.
On-Amazon Alexa for Shopping comparison and narrowing. Alexa for Shopping becomes the “expert friend” interface where shoppers compare shortlists, validate trade-offs, and ask follow-up questions that Amazon search never captured.
Clunky cross-platform buy-it-now flows. Early implementations like OpenAI’s ACP with Walmart catalog or Google’s UCP with Wayfair technically work, but in our view, they still feel more like experiments than a clearly better experience than a normal Amazon purchase.

From a planning standpoint, that means agentic commerce is already shaping consideration and discovery, even if the transaction still looks familiar. Brands that measure only the last click will miss where preferences are actually being formed.

Why Amazon’s Closed Loop is Actually the Near-Term Advantage

Outside Amazon, the ecosystem is fragmented. Different agent protocols, different browsers (Comet, Chrome with Gemini, Edge with Copilot), and different LLMs each have their own citation patterns, data partnerships, and quirks. That fragmentation is a tax on user experience and on marketers trying to build cohesive measurement.

Amazon, by contrast, controls a closed loop: Alexa for Shopping for conversation, COSMO for product understanding, Amazon checkout for transactability, and Prime for fulfillment. That vertical integration lets Amazon iterate faster on agentic experiences, because it owns the whole stack from prompt to package. The trade-off is that brands discoverable only through Amazon’s agents are fully exposed to Amazon’s commercial decisions, such as policy changes, ad formats, measurement access, and crawler rules.

As a media agency, we see this as both an opportunity and a risk. For Amazon-first brands, Amazon’s closed loop can act as a tailwind if you’re aligned with how Alexa for Shopping and COSMO see your catalog. For omnichannel brands, the risk is concentrating too much discovery power in a single walled garden while the rest of the agentic ecosystem is still being defined.

The Bull and Bear Case for Amazon Agentic Commerce

Bull Case: Why It Eventually Works

On the optimistic side, we see three structural advantages for Amazon-driven agentic commerce:

Decline in research fatigue. Shoppers are drowning in tabs, reviews, and contradictory advice. A capable agent that cuts through the noise and synthesizes options is genuinely valuable, especially in complex categories.
Hyper-relevant curation. Niche products surface more effectively when an agent can filter across thousands of SKUs on highly specific constraints such as budget, space, materials, health considerations, rather than relying on blunt category filters.
Autonomous negotiation and budget optimization. Prompts like “I have $100 and here are 15 things I want” are fundamentally new behaviors that traditional search interfaces don’t support well.

External analysts are reading the same signals. McKinsey, for example, estimates that AI agents could mediate up to $1 trillion in US B2C retail revenue by 2030, with global agentic commerce volume reaching $3-$5 trillion. We agree with that directionally, but our client work tells us the path there is uneven—especially on Amazon, where Alexa for Shopping and COSMO sit inside a very specific, retail-media-driven incentive structure.

Bear Case: Why it’s Clunky Right Now

On the skeptical side, we’re equally clear-eyed:

Hallucination risk. Agents still invent product details and misinterpret constraints. Trust has to be earned gradually, not assumed.
Echo chambers. Personalization can become a bias loop. In a webinar above, we posed an example using Ashley Furniture, where a prior query influences an unrelated recommendation six months later, which isn’t an outlier; it’s how many models behave today.
Privacy trade-offs. Agents work better the more they know about you, but “temporal privacy” tooling, where you share context for a short window without building a permanent profile, barely exists.
Disintermediation. When the agent owns the journey, brands risk losing the direct customer relationship and some of the signals they’ve historically used for lifecycle marketing.
Form factor. People still like to “cut across the grass.” Many shoppers will continue to bypass sophisticated flows when they already know what they want.

We don’t believe this tension resolves cleanly anytime soon. And we think that’s exactly why brands need a pragmatic playbook, not extremism.

“We built this great path. It’s a nice lovely paved concrete path around the outside — agentic commerce, all these bells and whistles. And the consumers are sitting there going, ‘I just want that thing. I just want to cut across the grass.“
Simon Poulton, EVP Innovation & Growth

Tinuiti’s View: Long-Run Bull, Short-Run Bear

Our stance is straightforward. In the long run, we’re bullish on agentic commerce as part of Amazon’s core shopping experience. In the short term, we’re realistic about the friction, legal battles, measurement gaps, and UX oddities that still need to be worked out.

That’s why we’re orienting clients around what can be controlled today: how Alexa for Shopping sees your products, how COSMO interprets your PDPs, how your brand shows up in AI citations and mentions across the web, and how robust your measurement stack is when clicks become partial or invisible. The brands that do that work now will be best positioned regardless of how fast Alexa for Shopping becomes fully transactable.

Theme	Bull Case	Bear Case	What We Recommend Now
Shopper Effort & Trust	Agents cut research fatigue and simplify decisions by summarizing reviews, specs, and trade-offs into a single, guided experience.	Hallucinations and opaque logic still erode trust; shoppers hesitate to hand over full purchase control.	Treat agents as advisors, not closers: structure PDPs to answer who/when/why/with/what so Alexa for Shopping can confidently recommend you, while keeping human-friendly detail intact.
Discovery & Curation	Hyper-relevant curation surfaces niche products that match tight constraints (budget, space, materials, health), expanding the long tail.	Personalization can create echo chambers where prior exposure keeps reinforcing the same brands and SKUs.	Invest in off-Amazon visibility (PR, reviews, social) so LLMs “know” your brand, and use Alexa for Shopping prompt reports to spot content gaps and new comparison frames.
Checkout & UX	Fully transactable Alexa for Shopping could make “I have $100 and 15 needs” a normal behavior, collapsing the funnel into a single interaction.	Cross-platform flows (external agents → retail sites) are still clunky and often worse than a standard Amazon purchase.	Assume near-term transactability stays inside Amazon: optimize for Alexa for Shopping -on-Amazon first, treat external protocols and Buy with Prime as optionality bets, not your core plan.
Platform Economics	Amazon’s closed loop (Alexa for Shopping → COSMO → checkout → Prime) can drive consistency and investment in richer agent experiences.	Agentic commerce threatens retail media economics when purchases shift to external agents that bypass Amazon’s ad stack.	Plan for a Alexa for Shopping -centric world: lean into Amazon retail media (DSP, Sponsored Products) while monitoring how agent surfaces affect category, brand, and non-brand performance.
Data & Measurement	AI-shaped journeys can still be measured via AMC, MMM, and AI visibility metrics, giving a richer picture of influence than last-click.	Traditional KPIs (organic traffic, ACOS, last-click ROAS) degrade as answers move into AI layers and clicks become partial.	Rebuild your measurement stack now: use AMC to map paths, establish incrementality baselines, and track AI visibility/citation share so you can see agentic impact as it grows.

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The 2026 Playbook for Amazon Brands

Get Alexa for Shopping -Ready Now, While the Rails Are Still Forming

The first move is foundational: rebuild your top-ASIN PDPs for LLM ingestion using the COSMO checklist above. Start where you already spend the most and prioritize outcome-led bullets, explicit use cases, constraints, and refreshed top-three images.

In parallel, start auditing Alexa for Shopping prompt reports weekly. Even without direct bidding levers, these reports provide a view into the conversational language shoppers use with Alexa for Shopping that standard search query reports simply don’t capture. We’re already using that data to shape content, to identify new negative keyword opportunities, and to pressure-test how well each client’s catalog is aligned to real-world questions.

Build Brand Preference Before Comparison Shopping Happens

Our AI in Search analysis reinforces a simple reality: by the time a shopper opens Alexa for Shopping , they’ve often already been influenced by what AI systems have surfaced about your brand across the wider web. LLMs weigh prior brand exposure—reviews, editorial mentions, Reddit threads, and TikTok content—when making comparisons.

That’s why we advise brands to invest in off-Amazon visibility that “leaks” into LLM training and real-time context: digital PR that earns mentions on trusted publishers, category-expert commentary, influencer content that shows up in transcripts and captions, and review presence that signals consistency. On Amazon itself, branded search is no longer a niche edge case—it’s where a growing share of high-intent traffic lands in 2026, and agentic systems are more likely to recommend brands they already “know.”

Treat Buy with Prime as an Optionality Bet, Not a Lead Play

Buy with Prime gives Amazon a technical rail for off-Amazon agentic checkout if external agents begin transacting at scale on third-party sites. For brands with meaningful DTC volume, we see value in piloting the integration, so you’re ready if and when agentic flows start to lean on those APIs as a fulfillment rail.

We don’t recommend building your entire agentic strategy around Buy with Prime today. Adoption is still relatively thin, and the more likely near-term path is for Alexa for Shopping -on-Amazon to become the primary agentic surface. Treat Buy with Prime as an option that expands your flexibility, not the central pillar of your agentic roadmap.

Set Up the Measurement Stack Before Alexa for Shopping Transactability Opens

Agentic commerce challenges many legacy measurement assumptions. That’s why we emphasize getting your measurement stack in place now, before Alexa for Shopping starts handling full transactions.

On Amazon, that means:

Using Amazon Marketing Cloud (AMC) to map the full path from upper-funnel exposure through Alexa for Shopping -influenced discovery to purchase, taking advantage of newer no-SQL interfaces where appropriate.
Establishing incrementality baselines on must-win non-branded terms and defensive branded terms now, so you can see how agentic surfaces change performance over time.
Shifting planning metrics toward TACoS and long-term ROAS rather than narrow ACOS, reflecting the reality that influence and visibility are increasingly happening in AI-shaped layers before the last click.

Watch Amazon’s IP and Policy Signals as a Forward Indicator

Finally, we treat Amazon’s IP, policy, and crawler moves as leading indicators of how agentic commerce will evolve. The Perplexity lawsuit, the tightening of robots.txt rules for AI crawlers, and the differing treatment of Googlebot vs. Google-Extended are all examples of how Amazon is actively shaping the agentic playing field in its favor.

For Amazon-first brands, that can be a tailwind as long as you’re aligned with Amazon’s preferred rails—Alexa for Shopping , COSMO, AMC, and prime-led fulfillment. For omnichannel brands, it’s a reminder to diversify and to think carefully about where you want agentic control to sit. We don’t expect a single universal agent to broker purchases across Amazon, Walmart, Target, and DTC anytime soon, because at least one major player has every incentive to prevent that reality from fully emerging.

Work With Tinuiti to Navigate the Agentic Era

Tinuiti is a media agency that not only builds brands, but also architects business outcomes. We create immediate and lasting growth for clients and end waste. Our Bliss Point Marketing Operating System—the ultimate brand and performance unifier—allows us to treat each client’s business as if it were our own, across audience, creative, media, and measurement.

In the agentic era, that operating system matters more than ever. We’re already helping brands:

Restructure Amazon PDPs and creative for Alexa for Shopping and COSMO.
Track AI visibility, citation share, and sentiment across platforms like ChatGPT, Perplexity, and Google’s AI experiences through our partnership with Profound.
Tie agentic influence back to real business outcomes using Amazon Marketing Cloud, MMM, and incrementality testing.

With over $4.5 billion in media under management and teams across the U.S., Mexico, and EMEA, we’re deeply embedded in the retail media and AI search changes that are redefining how growth gets built. If you’re ready to make Amazon agentic commerce work for your brand, our team is ready to help you architect the next phase of your business outcomes.