Nectar360 has revealed strong early results from campaigns on Pollen, its unified retail media platform, with leading global brands including Unilever and Coca-Cola Europacific Partners already seeing clear impact.

Less than a year since launch, Pollen is helping brands run more connected, effective campaigns – combining insight, media, and measurement in one place. Early feedback highlights a simpler, more joined-up experience and a clearer view of what drives results.

How Pollen Is Simplifying Retail Media With One Connected Platform

Retail media has grown quickly, but it can still be complex to plan, run, and measure campaigns. Pollen is designed to change that. Built in-house by Nectar360 and in collaboration with brands and agencies, it is designed to solve one of the industry’s biggest challenges: fragmentation.

Pollen brings together audience insight, media planning, campaign activation, in-flight optimization, and measurement into a single environment. Enabling brands to run connected campaigns across in-store, online, and off-site channels.

Importantly, at its core is a leading multi-touch attribution model. This gives brands a clear view of performance across the full customer journey, helping them understand what is driving growth and where to optimize for better results.

Amir Rasekh, Managing Director at Nectar360, said: “Pollen brings our audiences, media, creativity and measurement together in a way the industry never experienced before. What we are seeing now is proof that when you remove complexity through technology and focus on outcomes, brands can drive stronger, more measurable growth and deliver better customer experiences.”

Also Read: The Agency-Led E-commerce Model Is Changing

Strong Early Campaign Performance

• Over 2.5x higher incremental sales from omnichannel campaigns, with the strongest results seen when brands connect multiple touchpoints across the customer journey
• From those incremental sales, up to 25% are driven by mid and upper-funnel activity 
• Up to 10x higher conversion rates from targeted activity, powered by first-party data

Together, these results show how retail media can deliver beyond the shelf, driving measurable growth across the full customer journey.

Crucially, Pollen enables brands to measure true incremental impact, moving beyond siloed reporting and last-click attribution to understand what is really driving growth.

What Brands Are Saying

Charlotte Murphy, Head of Retail Media, Unilever UK and Ireland, said, “We are evolving our approach to retail media, with Pollen enabling a more connected and efficient way of working. This allows for faster activation and stronger measurement, marking a meaningful step forward in how we plan, activate, and optimize investment, with insights directly informing future decisions.”

Gemma Nicholas, Associate Director, Sales, Coca Cola Europacific Partners, added, “For big consumer and customer moments, multi-channel Retail Media campaigns like our Devil Wears Prada activation are essential to get right. Moving to Pollen means we can move faster as one team, including ourselves, Nectar360, WPP, and the Coca-Cola company to evaluate the campaign’s effectiveness together.”

Also Read: Dashboards Created Visibility, but They Didn’t Solve Commerce Execution

Optimized Creative AI That Connects Content to Outcomes

Pollen uses AI to improve performance, from pre-campaign planning to in-flight optimization, based on what is driving sales and incrementality.

This includes AI-powered tools such as advanced multi-touch attribution, giving a clearer view of how each channel contributes to results, and a creative checker, which can reduce compliance checks from weeks to seconds.

Nectar360 is working with PHD UK and its client, Lipton Teas and Infusions, to optimize creative messaging and drive improved retail media performance. Using Pollen’s creative optimization AI tooling, assets will be benchmarked and scored against years of historical insights to suggest improvements to help drive results against campaign objectives. This will enable brands to clearly identify what works and optimize their campaigns to drive more impact and deliver better business outcomes.

Lucy Holmes, Managing Partner at PHD UK, said, “Pollen brings together creative, targeting, performance and measurement. Our exclusive testing with Pollen will enable us to link creative performance with campaign outcomes and is a real step forward in helping clients to optimize impact and sales.”

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Agencies have filled the execution gap for CPG brands for decades, playing an integral role in content refreshes, retail media plans, and pricing updates across entire catalogs. But now, when AI agents can optimize a product page listing in seconds rather than the half-hour or more it takes a human to do it manually, the retailer-agency relationship is starting to evolve. It has to, in order to keep up with algorithms that reprioritize listings in real time, as category competition intensifies and rising CPCs create a more dynamic marketplace.

To keep up, brands have to replace the campaign cycle with agentic execution, using AI agents to continuously optimize content, media, and pricing across all SKUs and marketplaces. But that doesn’t mean agencies will no longer play an essential role.

Also Read: Dashboards Created Visibility, but They Didn’t Solve Execution

The Agency Model Wasn’t Built for How Retail Moves Today

According to a CommerceIQ survey, 76% of commerce teams still rely on agencies, with 49% allocating 15% to 30% of their budget to agency fees alone. Those retainers keep climbing, while 55% of commerce teams say agency costs are too high relative to results. The agency model was designed around campaigns with a plan, launch, and report at the end.

Now, a competitor can update their listing and take your search ranking overnight, outbid you for the top ad placement while your agency is working on the next report, and win the sale by the time your team sees that report. Commerce teams are already seeing this play out, with 40% saying their agency’s response times can’t keep up with how fast algorithms are already moving. 

Brands need to make thousands of daily optimizations to content, media, and pricing across a handful of marketplaces. This is far beyond what any team running on a campaign calendar can handle. The demand that marketplace algorithms generate is outpacing what agencies can deliver, so brands that want to stay competitive need to find a solution that will keep up.

How Agentic Execution Does the Work That Agencies Can’t

Agencies pull reports weekly to understand what happened, while AI agents analyze performance down to the minute and flag issues, such as out-of-stock listings, as they arise. Agencies build a plan and wait for approval, while AI agents recommend the next-best action and execute it within the brand’s guardrails. Agencies manually update listings one at a time, while AI agents adjust bids and optimize content across the full catalog around the clock.

The role of agencies needs to evolve. As these agents take on operational execution, agencies can stop billing for hours spent pulling reports and updating listings, and instead focus on work that requires a human touch. 

AI agents paired with human experts outperform traditional agency and SaaS workflows by 10x to 100x in speed and operational scale. While agencies are limited by human capacity and business hours, agents execute continuously across every marketplace where a brand is present. This looks like multiple agents that operate within defined guardrails and are trained on brand-specific context:

• A content agent identifies and resolves PDP gaps at scale. 
• A media agent improves iROAS via thousands of optimizations a day.
• A sales agent flags performance risks before they show up in a quarterly review. 

This scale of execution changes what’s possible at the catalog level. Agency-led teams can typically manage only the top 20% of a brand’s catalog, so the hero SKUs get the most attention, while the rest sit untouched, with unoptimized content, stale pricing, and missed media opportunities. Agentic execution covers 100% of SKUs without incurring the agency-hour scaling costs.

Agencies continue to add value through creative strategy, brand positioning, and integrated campaigns, while agents handle the operational work that never stops and that no agency can manually keep up with at scale.

Also Read: E-commerce Doesn’t Have a Data Problem. It Has a Speed One.

How the Agency-Led Model Is Evolving

The role agencies play in e-commerce is changing. Brands that still rely on them to manage their catalogs are spending more each year to cover only a small percentage of SKUs.

Meanwhile, the brands recognizing this early are rebuilding their ecommerce operations around continuous agentic execution and are already optimizing their full catalog across every marketplace at a speed and scale the old model isn’t set up to handle.

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Fast Simon introduced an AI personalization solution purpose-built for merchandisers at mid-market and enterprise e-commerce brands.

While most personalization technologies were originally built for massive marketplaces and ultra-high-traffic e-commerce environments such as AWS or Google, Fast Simon’s AI Personalization is architected for brands on Shopify. The model is optimized for the unique merchandising needs of mid-market and enterprise retailers, for which brand presentation, collection strategy, product launches, promotions, and inventory management are critical to conversion performance.

Early adopters of Fast Simon’s new AI Personalization for Merchandisers saw double-digit increases in revenue and conversion rates. 

Also Read: Are We Using AI to Help Customers or Avoid Them?

AI that Reduces the Operational Burdens of Merchandisers

Fast Simon’s AI Personalization for Merchandisers system uses behavioral signals to continuously adapt experiences across shopping surfaces while enabling merchandising teams to maintain strategic control over the customer experience. Without requiring any Personally Identifiable Information (PII), the model understands both shopping behaviors and merchandiser workflows, dramatically reducing manual curation, rule management, and other operational burdens while also significantly boosting shopper engagement, conversions, and revenue.

Latest Fast Simon AI Merchandising Breakthrough

Today’s news continues Fast Simon’s rapid delivery of no-code, retail-specific AI solutions specifically designed for merchandisers struggling with large product portfolios and catalogs, dynamic shopper behavior, and lackluster AI tools that continue to ignore or misunderstand their needs.

Also Read: Is AI About to Make Media Buying an Endless Experiment?

Earlier this year, Fast Simon enabled e-commerce merchandisers, for the first time, to quickly and accurately optimize products and collections for maximum conversion and revenue. The AI agent solution instantly shows which products are truly successful, overexposed, or hidden winners, and measures the short- and long-term opportunity costs of merchandising decisions across the portfolio.

“DTC brands don’t want generic AI personalization that ignores merchandising strategies like brand positioning and critical operational realities such as inventory,” said Zohar Gilad, CEO of Fast Simon, “While many AI commerce tools rely primarily on large language models and chat interfaces, Fast Simon’s AI personalization is built around a commerce-native operational model that continuously adapts to shopper behavior, merchandising strategy, and catalog dynamics in real time.”

Fast Simon AI Personalization for Merchandisers is now generally available for Shopify Plus merchants. 

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Gopuff, the leader in instant commerce, and GrowthLoop, a pioneer in agentic AI-powered marketing solutions, today announced a partnership to enable custom audience segmentation for Gopuff and BevMo! advertising partners. With GrowthLoop’s composable commerce media solution, brands advertising with Gopuff and BevMo! will be able to leverage new audience targeting, activate campaigns quickly, and gain visibility into campaign metrics.

“Retail media is one of the fastest-growing channels in advertising, but too many networks are limited by disconnected data and slow execution,” said Anthony Rotio, co-founder and co-CEO of GrowthLoop. “Gopuff has a unique advantage with high-intent customers making purchase decisions and a Gen Z audience that brands are eager to understand. By leveraging GrowthLoop, Gopuff’s partners can act instantly on first-party data to reach the right customers at the moment they’re ready to buy.”

With the new integration, Gopuff and BevMo!’s ad partners can move beyond broad category targeting to segment buyers by geography, purchase history, and order frequency, then activate those audiences via sell-side platforms (SSP) or demand-side platforms (DSP). For example, within a few days, a brand could request and launch an audience based on frequent buyers of its top five competitors living in college markets. Later this year, brands will also be able to target these audiences for sponsored product ads in the Gopuff app and utilize the new capabilities to target customers in the U.K.

“At Gopuff, our entire business model is built around precision and speed,” said JR Crosby, Director of Data Partnerships at Gopuff. “But historically, creating custom audience cohorts isn’t something brands can turn around quickly. By integrating GrowthLoop, we are now able to give brands exactly what they’ve been asking for: the ability to build and test with really granular audience segments both on Gopuff and through our marketplace partners, all with a turnaround time that was previously impossible.”

Also Read: AI Is Supercharging Returns Fraud. Retailers Are Behind.

GrowthLoop’s platform unifies audience creation, campaign activation, and measurement directly on Snowflake’s AI Data Cloud, eliminating the need to move data across systems and ensuring campaigns are powered by the most complete and up-to-date customer information. The integration is now live for Gopuff’s beta partners, with the new capabilities rolling out to all brands over the coming weeks.

As Gopuff continues to expand its retail media capabilities, its partnership with GrowthLoop supports its broader mission to deliver more personalized, relevant experiences to customers while creating new opportunities for brand partners to connect with its growing community.

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Kopa.ai, an agentic AI platform for e-commerce teams, has raised €2 million in seed funding, co-led by XTX Ventures and Practica Capital, with participation from Inovia Capital and angel investor Etan Ilfeld.

The company is building what it describes as an operating system for e-commerce businesses, designed to help teams delegate operational and analytical work to AI agents that can understand context, make decisions, and execute tasks autonomously.

Founded by a team with more than a decade of hands-on e-commerce experience, Kopa.ai is built on the idea that running a successful online business requires thousands of expert decisions every week. Rather than focusing solely on automation, the platform aims to enable merchants to delegate work to AI agents, just as they would rely on experienced internal operators.

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Kopa.ai connects directly to a merchant’s existing tools and storefront, continuously analyzing product, campaign, inventory, customer behavior, and site performance. Based on this understanding, its AI agents identify opportunities to improve business performance and take action accordingly — including generating creatives, adjusting campaigns, reallocating budgets, or publishing updates across connected systems.

The platform is designed to interpret intent rather than rely on prompts or predefined workflows. Teams provide high-level objectives, while the system determines how to execute them. Actions can run with human approval or autonomously, depending on customer preferences.

According to the company, every action and outcome feeds back into the system, allowing the AI to improve its judgment and execution over time through a continuous cycle of analysis, decision-making, execution and learning.

Also Read: E-commerce Doesn’t Have a Data Problem. It Has a Speed One.

According to Donatas Benaitis, founder of Kopa.ai, many e-commerce businesses have the potential to scale significantly faster, but are often slowed down by increasing operational complexity:

We’re building Kopa.ai to feel like handing work to your best expert – someone who understands what you’re trying to achieve from just a few words, makes smart decisions on your behalf, and delivers results that are often even better than you imagined.

Unlike point solutions focused on individual functions such as advertising, analytics or inventory management, Kopa.ai takes a broader approach across the entire e-commerce operation. Under the hood, the company is developing proprietary systems for structuring business knowledge, managing operational context and orchestrating specialized AI agents at scale.

The newly raised funding will be used to further develop the company’s core AI infrastructure, improve the intelligence and reliability of its agents, and expand its go-to-market efforts.

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A category manager pulls a report and sees that a hero SKU is missing an ingredient keyword that nobody caught, causing the listing that ranked No. 3 to drop to No. 8. Meanwhile, a competitor who adjusted their bids overnight took the top sponsored placement before anyone could react. 

The e-commerce advantage used to belong to whoever had the best data, but today, brands have more data than they can act on, and that edge has now shifted to execution speed. Retail algorithms are getting more sophisticated, retail media costs are rising, and competitor SKU counts keep increasing. Human teams operating on weekly review cycles can’t keep up, and no amount of improved dashboards or additional insights can help.

What these brands need is to adopt an agentic retail approach that moves them from analysis to action, with AI agents that work alongside human teams to execute at the speed that retail demands.

Also Read: Contextual Advertising: What It Is and Why It Matters

E-Commerce Doesn’t Have an Insight Problem. It Has an Execution Problem

CPG brands have more than enough data visibility, but not nearly enough time to act on it. In a recent CommerceIQ survey, 46% of CPG brands said their data isn’t actionable, and 42% said decisions take too long. Meanwhile, the tools brands have been relying on for the last decade aren’t built for the pace at which retail now operates. Dashboards show performance, while weekly reviews help teams plan what’s next. Neither was designed for immediate action, which is necessary to stay competitive in modern e-commerce.

Outsourcing also won’t help brands keep pace, since traditional media agencies can only prepare so many optimizations a day. Even with the additional help from agencies, there’s still a lag between finding a problem and fixing it. By the time a listing is updated, a competitor might have already swooped in. And according to the same survey, more than half of brands said agency costs are too high relative to results.

When a brand loses the Buy Box, a media campaign is outbid, or a stockout goes unnoticed, it might seem like a small issue. Multiply those missed optimizations across thousands of SKUs and a handful of marketplaces, and these seemingly minor problems add up to real revenue that’s regularly moving to competitors.

But how are these competitors moving so quickly? They’re no longer manually digging through their data to diagnose issues, only to wait for human teams to resolve them. They’re using AI agents to act in real time.

Agentic Retail Changes What Execution Looks Like

Agentic retail refers to the use of AI agents to analyze, decide, and execute across thousands of SKUs at once. With agentic retail, instead of teams reacting to their dashboards, they direct agents to act on what’s happening in real time.

These AI agents work across content, pricing, media, and availability 24/7 within the brand-defined guardrails. This looks like: 

• A content agent identifies and resolves PDP compliance and optimization gaps for SEO, AEO, and search visibility across every marketplace.
• A sales agent monitors real-time sales performance and recommends actions to close sales gaps before they impact quarterly results.
• A shelf agent monitors content, availability, assortment, and reviews to identify opportunities for optimization.
• A media agent optimizes retail media performance by leveraging dozens of signals at a scale that no manual process can match.

Each agent works continuously, across the full catalog, not just the top-performing SKUs.

This doesn’t take internal teams or agencies out of the equation; it just means brands can remove them from repetitive tasks and use their time more effectively on work that requires strategy and human judgment. 

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Agentic Retail Execution Is the New Competitive Advantage

The brands still pulling reports from their dashboards and increasing their agency retainers are solving a problem from the last decade. The ones investing in agentic retail execution are solving the one that will define this one.

It’s during the time between insight and execution that e-commerce performance is won or lost. The speed required to compete has outpaced what any manual process or agency can deliver. If brands can’t execute at the speed marketplaces operate, then their competitors will, taking the sale, the ranking, and the customer with them.

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Refund and replacement claims, from “this serum gave me a rash” to “the palette arrived completely shattered,” are on the rise, each accompanied by photos and screenshots as proof. The influx is driven by a new returns fraud tactic, one where abusers are doctoring images using AI to make false claims seem more convincing.

Image manipulation with AI is now contributing to the $850 billion returns problem companies experience each year, with almost $77 billion of those returns being fraudulent. AI is helping bad actors polish their stories while revealing a new blind spot for many retailers who are unable to detect when AI is used in claims or to assess its impact on their post‑purchase margins.

Many brands are responding by tightening their policies, adding return fees, and asking customers to jump through more hoops to verify their claims. But this approach creates an even greater liability: losing loyal customers who become frustrated as they’re treated the same as abusers.

Also Read: The Death of Batch-and-Blast Email Marketing

AI Is Stress-Testing Retail Blind Spots

Fake proof is cheap and easy to produce with AI, allowing the same offenders to repeatedly send slightly different, altered photos and stories that look like real customer photos. 

With AI, post-purchase abuse can look like: 

• Generate or edit damage photos to add cracks, leaks, or broken packaging.
• Creating staged “stolen package” images that show an empty doorstep or an opened box with items missing.
• Producing fake drop‑off receipts as proof that an item was sent back when it never left the customer’s home
• Writing policy-aware emails and chat messages that align with current refund and replacement policies that make it hard to say “no.”

Meanwhile, customer service teams are now serving as fraud investigators. When a bad actor opens a ticket, attaches a photo, and explains what went wrong, reps can no longer simply follow the policy, which often states that a refund, replacement, or credit should be issued when the claim appears to meet the rules. Those rules were written for a world where proof was harder to fake, and few brands have a reliable way to tell when an image or story has been manipulated with AI.

Most retailers don’t have a consistent, cross‑team way to flag suspicious content and compare it to past behavior. Support teams see individual cases, often from what appear to be different customers. But in reality, the same person may be creating new profiles and repeating the same AI‑assisted claims, which is impossible to spot in a single ticket.

Rethink How You Respond to AI-Driven Return Fraud

The knee-jerk reaction for brands is to tighten return policies and train agents to say “no” more often. That feels like taking control, but it punishes loyal customers while determined abusers simply upgrade their AI tools and create more accounts to keep going.

Fraudsters will only find new and creative ways to use AI. Today, they’re generating fake photos, but tomorrow, they’ll use a new tactic. This is why retailers need to stop treating each claim in isolation and instead look for important patterns. How often does a customer report an issue? What kinds of issues do they report? How do their claims compare to those of other customers?

Use AI to Detect AI-Assisted Abuse

Retailers need the ability to connect orders, returns, claims, support tickets, credits, and even basic interaction patterns (like device and address history) into a single view of each customer. Then, flag behaviors that deserve a closer look, such as repeated “item not received” claims, frequent high‑value “damaged” reports, or clusters of accounts sharing the same details.

Update Your Policies Based on Behavior

Make policies more dynamic, rather than stricter across the board. Trusted customers, those with normal return behavior, can continue to enjoy fast, generous resolutions with minimal friction. Meanwhile, high‑risk customers can be routed into different flows, such as extra checks before issuing a refund, different return options, smaller credits, or, in some cases, blocked future claims.

Over time, that kind of behavioral playbook does more to protect both your margins and your best customers than another round of blanket crackdowns.

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Respond to AI Tactics With a Strong System of Defense

AI tactics in return fraud will continue to evolve, and they’re already forcing brands to confront how little they truly understand about post-purchase behaviors. But treating every customer like a suspect won’t fix retail’s returns fraud problem.

You can keep adding restrictions and hope your best customers tolerate the extra friction, or you can invest in technology that shows customer behavior end‑to‑end. Your team needs a defense system that can keep pace with these ever-evolving AI-driven return-fraud tactics. That way, claim resolution becomes less about the believability of a one-off story or supporting imagery and more about the identified patterns in how a customer shops, returns, and engages with support.

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Amazon has launched a 30-minute delivery service called Amazon Now, rolling it out across dozens of American cities as the company escalates its push into the ultra-fast delivery market long dominated by DoorDash, Uber Eats, and Instacart.

The service, available through the Amazon app and website, covers thousands of items spanning fresh groceries, household essentials, electronics, personal care products, and alcohol where permitted. Eligible items are flagged with a “30-minute delivery” banner, and Amazon Now offers are surfaced to customers as they browse.

How It Works

Amazon Now is live in Atlanta, Dallas-Fort Worth, Philadelphia, and Seattle, with expansion underway in Austin, Denver, Houston, Minneapolis, Oklahoma City, Orlando, and Phoenix. The company expects the service to reach tens of millions of customers across these and additional cities by year-end.

The speed is enabled by a network of smaller fulfillment locations, positioned closer to residential and commercial areas than Amazon’s traditional warehouse infrastructure. Reduced travel distances, combined with a curated selection of high-demand items, enable the company to compress delivery windows to 30 minutes or less. In most markets, the service operates around the clock.

Also Read: Merchandisers Are Drowning in Data and Still Flying Blind

The Price Argument

Amazon Now is not free, but the company is making a deliberate pricing case against its competitors. Prime members pay a flat $3.99 per order, compared with $13.99 for non-Prime customers. Orders below $15 carry a small additional fee of $1.99 for Prime members and $3.99 for non-members.

That structure is notably more transparent than the variable pricing models common among rivals, which typically layer on delivery fees, service charges, expected tips, and, in some cases, per-item price markups. For Prime members placing regular orders, the math frequently favors Amazon Now.

A Broader Speed Ecosystem

The launch extends an already substantial fast-delivery infrastructure. Amazon currently offers one-hour and three-hour delivery across more than 90,000 products, same-day delivery across millions of items, and drone delivery trials in eight US locations through its Prime Air program, targeting sub-60-minute windows.

The scale of that infrastructure is considerable. In 2025, Amazon Prime members received more than 13 billion items via same-day or next-day delivery globally. The United States accounted for eight billion of those deliveries — a figure 30 percent higher than the year prior.

“Amazon Now is for when you need or want the convenience of getting your Amazon order delivered in 30 minutes or less,” said Udit Madan, Senior Vice President of Amazon Worldwide Operations. “You can get everything from groceries for dinner, to AirPods before a flight, to household essentials like laundry detergent delivered right to your door.”

Also Read: Your Homepage Isn’t the Front Door Anymore

The Competitive Stakes

Amazon began piloting 30-minute delivery in Seattle and Philadelphia in December, a move widely interpreted as a direct challenge to the quick-commerce platforms that have built significant consumer habits around on-demand delivery. With Amazon Now now scaling nationally, those platforms face a competitor with deeper logistics infrastructure, a larger existing customer base, and a membership program that makes the economics of fast delivery considerably more favorable for tens of millions of American households.

The race to own the last 30 minutes of retail is no longer a side experiment. For Amazon, it is becoming core infrastructure.

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A London-based advertising technology company has launched a native ads application for Shopify, aiming to give merchants more precise control over how they reach and recapture potential customers.

Preciso announced on Tuesday the availability of Ultima Ads for Advertisers on the Shopify App Store, billing it as the first native advertising plugin for the platform built specifically around a merchant’s campaign goals. The app connects directly to a merchant’s Shopify dashboard, allowing them to manage targeted advertising campaigns without switching between platforms.

By syncing product feeds, purchase history and browsing behavior, merchants can build custom audience segments, retarget visitors who browsed without buying, and construct a lookalike audience modeled on their most valuable existing customers.

Ultima is Preciso’s flagship native advertising product. It uses machine learning to generate personalized ad creatives and place them contextually within web pages — an approach designed to reduce banner blindness, which has blunted the effectiveness of traditional display advertising.

The company says internal testing shows the product outperforms industry benchmarks across key metrics: a click-through rate of 2.1 percent against an industry average of 1.84 percent, an engagement rate of nearly 66 percent compared with 60 percent for the sector, and an average session duration of three minutes and 46 seconds, roughly a minute longer than the industry norm.

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“Ultima Ads for Advertisers has been designed from the ground up to enable Shopify merchants to use premium native placements that target users in a non-intrusive manner,” said Piero Pavone, Chief Executive of Preciso. “Thanks to a very simple Shopify integration, merchants can get campaigns live in a few hours.”

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AEON360 and Google Cloud have announced a multi-year collaboration to build an AI-driven commerce platform across AEON’s retail, finance, and lifestyle ecosystem in Southeast Asia, beginning in Malaysia.

The partnership centers on what the companies are calling a continuous commerce experience — a system designed to carry a shopper’s context, preferences, and history seamlessly across every AEON interaction, from product discovery through checkout, financing, and post-purchase support, without requiring them to re-establish their identity or preferences at each step.

At the core of the initiative is a contextual intelligence engine built on Google Cloud’s BigQuery data platform. AEON360 is using it to construct an enterprise knowledge graph that reconciles customer data across its various entities and touchpoints. In practice, a shopper searching for groceries in Kuala Lumpur would receive curated product recommendations, member pricing, and real-time stock availability drawn from their purchasing history — alongside dynamically integrated financing options such as installment plans, cashback, and loyalty points.

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“Our ambition is to create an ecosystem that serves consumers as a cohesive, intelligent entity, so every engagement feels crafted just for them, no matter where they begin their shopping journey,” said Daisuke Maeda, chairman of AEON360. “We’re shifting from simple digital interactions to AI agents that surface the most relevant offerings and perform complex tasks on our customers’ behalf.”

To support the rollout, Google Cloud will help establish the AEON360 Innovation Foundry in Kuala Lumpur — a dedicated center designed to train AEON employees in AI fluency and equip teams across its lines of business with Google skills and certifications. The Foundry will also serve as an environment for developing and scaling agentic AI solutions across AEON’s merchant and supplier ecosystem.

The platform will deploy two AI agents built on Google’s Gemini Enterprise for Customer Experience. The first is a shopping agent that acts as a digital concierge — processing text, voice, and image inputs to autonomously build carts and execute transactions on a customer’s behalf. The second is a customer experience agent providing round-the-clock query resolution while offering real-time guidance to human representatives.

AEON360 also plans to extend its agentic commerce capabilities beyond its own digital properties by adopting the Universal Commerce Protocol, an open standard pioneered by Google that allows AI agents and retail systems to operate across different consumer surfaces, businesses, and payment providers. The company is additionally exploring integrations with Google Pay that would allow shoppers to authorize agent-led transactions across touchpoints, with payment and shipping information stored securely in Google Wallet.

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“For too long, traditional retail technologies have created fragmented experiences where shoppers are forced to restart their journeys at every turn,” said Hana Raja, country manager for Malaysia at Google Cloud. “Our collaboration will deliver agentic commerce solutions grounded in a continuous thread of context — ensuring that information flows with the shopper to help AEON360 anticipate and act on what they need next.”

The collaboration will initially focus on Malaysia, with a roadmap to expand across other Southeast Asian markets where AEON operates.

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