Algolia, the AI Search and Retrieval platform orchestrating more than 1.75 trillion queries each year, trusted by over 18,000 businesses and used by millions of developers worldwide, announced significant enhancements to  its Shopify integration. The updates further strengthen Algolia’s position within the Shopify ecosystem and deliver faster performance, deeper merchandising control, and stronger scalability for growing brands.

The release introduces Commerce Pipeline, a new indexing foundation that dramatically improves speed and reliability, as well as enhanced analytics, campaign-driven merchandising, structured category support, and richer content discovery. Native Horizon theme and Native Virtual Replica Support will follow this summer.

Nate Barad, Vice President of Product and Technical Marketing, Algolia, said: “Together, these innovations give Shopify merchants a faster, smarter search experience that directly supports revenue growth. We are aligning tightly with Shopify’s evolution while giving merchandisers more control, better data, and enterprise-grade performance.”

Also Read: Topsort Launches Tomi, ​​an AI Agent to Automate Retail Media Campaign Operations

Commerce Pipeline: Built for Scale 

At the core of the expansion is Commerce Pipeline, a next-generation indexing architecture replacing Algolia’s previous system, and a foundational upgrade for Shopify and its merchants. Search now keeps pace with merchandising, international expansion, and peak demand without costly workarounds.

For large catalogs, full reindex times have dropped by upwards of 80 percent, from over 45 minutes to under 10 minutes. Throughput has increased by more than 50 percent, and for Shopify Markets merchants, product updates now reflect in an average of two minutes. Metafield-heavy stores can now complete full reindexes reliably, and the previous 10-market limit has been removed.

One-Click Pixel Activation: Smarter Data, Better Relevancy

Algolia’s new Click-to-Activate Pixel Analytics captures shopper behavior, including clicks, add-to-carts, and purchases, in a single step.

For merchandisers, this means clearer insight into product performance and stronger data to guide ranking, promotions, and campaign decisions. Behavioral signals automatically improve relevancy, making advanced features available immediately without additional tracking projects.

Barad added, “When shopper data flows seamlessly into search, results improve from day one, Merchandisers can optimize faster and drive stronger performance.”

Enhanced Autocomplete, InstantSearch, & Analytics for Shopify App Blocks

Algolia expanded customization within Shopify App Blocks, giving merchants greater control over how search behaves without requiring a storefront rebuild. Merchants can now pass advanced parameters, including analytics tags, directly into search configurations, enabling more precise tracking and smarter optimization.

This gives merchandisers clearer direction over what search prioritizes and measures. They can align search with campaign goals, track what matters most, and launch faster without complex technical projects. It’s less operational overhead and more measurable impact on what shoppers see and buy.

Dynamic Contexts for Collection Page Merchandising

Algolia enables merchants to apply dynamic rule contexts directly to collection pages, unlocking true page-level merchandising control. Rule contexts act as smart triggers that activate specific merchandising rules, such as pinning products, boosting categories, hiding items, applying filters, or displaying banners, based on the situation.

Barad added: “This means the same collection page can deliver different experiences depending on how a shopper arrives or what campaign is running. For example, a Women’s Shoes collection can prioritize clearance items for email traffic, boost new arrivals for homepage visitors, or highlight a featured brand during a seasonal promotion, all without changing the underlying collection. Merchants gain campaign-specific control over product ordering and presentation, supporting paid media, organic traffic, and promotional pushes with precision.”

This enhancement gives merchants a stable, supported way to tailor collection pages dynamically while keeping their merchandising strategy agile and aligned to business goals.

Metaobject Support for Richer Content in Search

Algolia now indexes Shopify Metaobjects, allowing buying guides, fit details, ingredients, brand stories, and promotional content to appear directly within search and category experiences.

Barad noted: “Content no longer sits on the sidelines, instead it becomes part of the merchandising engine, helping shoppers discover not just products, but the context that drives confidence and conversion.”

Hierarchical Category Support for Merchandising Studio and Query Categorization

Algolia’s Shopify connector now indexes Shopify’s Standard Product Taxonomy, bringing full parent-child category hierarchies directly into search and merchandising workflows. Instead of relying on flat collection data, merchants can now leverage structured category paths such as Apparel & Accessories > Clothing > Activewear > Tops, enabling deeper and more precise merchandising control.

This enhancement allows Merchandising Studio and Query Categorization to operate with full category depth, unlocking multilevel facets and AI-driven optimizations that depend on hierarchical structure. Because it uses the category data merchants already maintain within Shopify, no additional data entry or complex mappings are required. The result is more intuitive category-based merchandising, cleaner navigation, and smarter query understanding without added operational overhead.

SOURCE: Algolia

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Topsort, an AI-native, auction-based retail media infrastructure company powering marketplaces and retailers worldwide, announced the launch of Tomi, an AI agent designed to simplify retail media ad operations by enabling teams to create and manage ad campaigns using natural language.

As retail media programs scale, ad operations teams are responsible for managing an increasing number of advertisers, campaigns, and complexity. Traditional campaign setup often requires manually configuring products, budgets, targeting, and schedules across multiple vendors.

Tomi streamlines this workflow into a conversational interface. Instead of building campaigns step by step, users can simply describe their objective, and Tomi generates the campaign configuration for review and approval before launch.

Also Read: Similarweb Expands Ecommerce and Digital Shelf Analytics with Retail Intelligence Suite

“Retail media teams are under pressure to support more campaigns without increasing operational overhead,” said Regina Ye, CEO of Topsort. “Tomi reflects our vision for AI-native infrastructure, where intelligent agents help marketplaces operate more efficiently while keeping retailers fully in control of their monetization strategy.”

Faster Campaign Launch with AI-Assisted Ad Operations

Embedded directly within the Topsort marketplace admin interface, Tomi allows teams to create and manage campaigns using plain-language prompts.

For example, a user might ask:

“Create sponsored listing campaigns for the top-performing SKUs in electronics with a $10,000 budget running for the next two weeks.”

Tomi then identifies relevant products, configures campaign settings, applies targeting rules, and sets the campaign budget and duration. The user reviews and approves the campaign before it goes live.

Through this AI-assisted workflow, retail media teams can:

Launch campaigns up to 5× faster

Generate campaigns in seconds instead of manually configuring products, budgets, dates, bids, and targeting settings.

Make more informed campaign decisions

Use product performance signals and marketplace insights to identify what to promote.

Scale operations more efficiently

Manage more campaigns across more vendors without increasing operational workload.

Maintain full control

Review and approve campaign setups before anything goes live.

Built for the AI-Native Future of Retail Media

Tomi’s initial release focuses on onsite sponsored listing campaigns, supporting keyword, category, competitor-page, and always-on targeting strategies.

The launch reinforces Topsort’s mission to build AI-native retail media infrastructure that helps enterprise marketplaces scale advertising programs without the complexity of legacy ad tech.

SOURCE: AccessNewswire

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Pacvue and Reddit have announced a new partnership that will bring Reddit Ads into Pacvue’s Commerce Operating System. The integration, launching this spring, will combine Reddit’s community-driven reach and high-intent audiences with Pacvue’s AI-powered platform, used by enterprise brands and agencies to plan, activate, and optimize full-funnel performance at a global scale.

“Reddit plays a critical role in how consumers evaluate products today,” said Rahul Choraria, Chief Executive Officer, Pacvue. “Our goal is to make that influence easier to operationalize at scale. This partnership gives brands the tools to manage Reddit alongside retail media within a single, governed system.”

With the rise of social platforms and discovery commerce, purchase decisions are increasingly shaped by peer discussion, reviews, and community validation. Attracting 121 million daily active unique visitors to over 100,000 active communities, Reddit is a rich well of commerce signals, with 90% of its users saying they trust the platform as a resource to learn about new products and brands (Reddit for Business). For brands and agencies, platforms like Reddit are becoming central to full-funnel strategy.

Also Read: Similarweb Expands Ecommerce and Digital Shelf Analytics with Retail Intelligence Suite

As retail media strategies shift upstream into community-driven platforms, however, operational scale has become a constraint. Brands need to connect discovery, consideration, and conversion across channels, but the systems supporting those channels are often working in silos. For enterprise teams managing multiple brands, markets, and stakeholders, this fragmentation creates challenges in governance, budget pacing, and cross-channel measurement. Performance insights may exist, but without a unified system to prioritize and operationalize them, optimization slows and opportunity is lost.

Reddit offers unique opportunities for brands to tap into a high-intent, high-trust environment at key moments of discovery when consumers are actively seeking recommendations. By integrating Reddit Ads into Pacvue’s platform, this partnership will make it easier for brands to leverage those opportunities across markets and alongside the rest of their full-funnel media mix. In addition to unified reporting and measurement capabilities that offer cleaner rollups and faster insight delivery, the new integration enables:

• Enterprise-scale campaign management: Streamlined creation and editing across a broad range of Reddit Ads formats — including Product, Video, Image, Carousel, and Free-form Ads — designed to support governed execution across brands, markets, and teams.
• Advanced audience activation: Flexible management of Reddit’s targeting capabilities, including interests, communities, keywords, automated targeting, and custom audiences, within a standardized operational framework.
• Budget governance and pacing control: Portfolio-level budget management, tagging, dayparting, and automation to maintain pacing discipline and reduce manual work.
• Automation and decision intelligence: Rules-based execution and prioritized recommendations that accelerate optimization and turn insights into action more quickly.
• Creative intelligence built for community context: Advanced creative analysis extending Reddit’s Ad Inspiration Library, delivering actionable insights that connect creative attributes to measurable performance outcomes.

Through their partnership, Reddit and Pacvue are giving brands and agencies the ability to bring a major conversation-driven channel into the same operating system that unifies execution across 100+ retail media networks and 30+ markets, reducing fragmentation and accelerating decision-making.

SOURCE: Pacvue

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Similarweb launched a new Retail Intelligence suite of retail analysis tools combining Amazon IQ with Cross-Retail IQ coverage of 650+ online stores and marketplaces. New digital shelf analysis capabilities provide an understanding of product mix, availability, and pricing across stores and marketplaces.

The unified view that Similarweb Retail Intelligence creates is more important than ever as the AI-driven revolution continues to shape how consumers discover products and decide whether or where to buy them. Combined with the growth of retail media and marketplace fragmentation, brands and sellers are finding it harder than ever to understand where demand is forming, how competitors are winning, and what actions they can make to improve sales performance. The emerging Universal Commerce Protocol, which allows AI agents to make purchases from online stores, signals yet another transformation that will make the kind of content, stock, availability, and pricing optimization Retail Intelligence enables more important than ever.

“AI is transforming the industry, but equally significant is how online retailers are reshaping their business models by following Amazon’s lead, operating as marketplaces and launching retail media networks,” said Marta Sulkiewicz, VP, Emerging Solutions at Similarweb. “That means brands need to understand what shoppers need, where they are, how to improve product visibility, and where and when to invest in retail media. Retail Intelligence combines shopper behavioral data with digital shelf and pricing signals to give companies a complete view, helping them turn insight into strategy and execution.”

Also Read: Shoplazza Adopts Agentic Commerce Architecture to Power AI-Driven E-commerce Operations

For more than five years, Similarweb has been helping brands understand how to grow their sales on Amazon while helping retailers compete better in an online world where Amazon is a dominant player. Amazon IQ continues to deliver analysis of the world’s largest online marketplace for the US, the UK, Canada, France, Germany, and Italy.

Retail Intelligence adds Cross-Retail IQ product, category and brand analytics for more than 650 global retailers. This also includes on-site search keyword optimization to surface what consumers are actively searching for that lead them to specific products.

New digital shelf analytics provide in-depth analysis of product availability and pricing to Cross-Retail IQ that Similarweb previously offered only for Amazon. In addition, both Amazon IQ and Cross-Retail IQ now offer the option of digital shelf automation that brands can employ to automatically reprice products as competitive dynamics change. That kind of automation can be the only way of staying competitive during a price war or retaining a favored “buy box” placement as the top recommended product in a category.

Retail Intelligence provides brands with clear content and pricing recommendations to improve product discovery and sales. Retailers can use the same data to identify gaps in product assortments and in-demand brands, while benchmarking inventory and pricing against the competition and optimizing their own performance.

“Similarweb’s Retail Intelligence acts as our lighthouse. It gives us the clarity to see category shifts across brands, on-site search consumer behavior, and competitor dynamics – all in one view,” said Ben Kaminski, Cofounder and Chairman at Essor, which is in the business of nurturing and growing online businesses.

SOURCE: BusinessWire

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Toshiba Global Commerce Solutions, a leader in retail technology, announces the appointment of Trish Nettleship as Senior Vice President of Marketing, effective immediately. In this role, she will serve on the company’s executive leadership team and lead the global marketing organization, responsible for defining and executing the company’s marketing vision and strategy. With more than 25 years of marketing leadership experience across global technology and SaaS organizations, Nettleship will oversee brand strategy, product marketing, demand generation, communications, analyst relations, and customer engagement across Toshiba’s global portfolio of retail solutions.

“Trish Nettleship is a proven marketing leader with deep experience in retail technology and platform-based businesses,” said Rance Poehler, President and CEO at Toshiba Global Commerce Solutions. “Her ability to align marketing and sales, elevate global brands, and drive measurable growth through modern, data-driven teams and AI-enabled marketing systems makes her an outstanding addition to our leadership as we continue to innovate for retailers around the world.”

In this role, Nettleship will help sharpen Toshiba’s market leadership by strengthening the company’s brand and position as a trusted partner to retailers navigating rapid change across grocery, hospitality, specialty, and convenience. By working closely with cross-functional teams, she will ensure Toshiba’s go-to-market strategies are tightly aligned with its innovation roadmap, bringing solutions to market that help retailers simplify operations, enhance the shopper experience, and unlock new growth opportunities. Her leadership will further connect Toshiba’s technology innovation with the real-world needs of retailers, reinforcing the company’s commitment to delivering scalable, future-ready commerce solutions.

Also Read: Noibu Launches Unified Ecommerce Analytics & Monitoring Platform

“The retail industry is being reimagined in real time, and I’ve spent my career at the intersection of that transformation. What drew me to Toshiba is the genuine innovation happening here with solutions that are powering some of the world’s most complex retail environments. My focus is simple: elevate that story, sharpen the brand, and make Toshiba the name retailers think of first when the stakes are highest,” said Nettleship.

Prior to joining Toshiba, Nettleship served as Chief Marketing Officer at NCR Voyix, where she led a global marketing and communications organization and helped transform the company’s brand and go-to-market strategy to support a SaaS-first platform. She also held senior marketing leadership roles at ResMed, including Chief Marketing Officer for its SaaS business and Vice President of Marketing for the Brightree division.

SOURCE: BusinessWire

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Shoplazza, a global commerce platform serving direct-to-consumer (DTC) brands, announced the adoption of an Agentic Commerce architecture across its platform, introducing AI agents that can directly execute operational tasks within e-commerce workflows.

Agentic commerce shifts e-commerce from tool-based management to goal-based execution. Instead of navigating multiple dashboards or configuring workflows step by step, merchants can simply describe the business outcome they want, and the platform coordinates the necessary actions across storefront operations, payments, marketing tools, and customer management systems. The platform can automatically execute tasks such as creating campaigns targeting specific customers, updating storefront merchandising, launching campaigns, and monitoring performance.

At the core of this capability is Shoplazza’s proprietary Ecom Agent orchestration layer, which coordinates how AI agents interact with the platform’s commerce systems. This infrastructure allows agents to securely access different parts of the platform and execute tasks across storefront management, payments, marketing, and customer data while maintaining merchant-defined permissions and safeguards. The platform also evaluates results using real transaction data, enabling the system to continuously improve how similar tasks are executed over time.

Also Read: Noibu Launches Unified Ecommerce Analytics & Monitoring Platform

A recent industry report from 36Kr highlights Shoplazza as an example of how commerce platforms are evolving toward Agentic Commerce. The report notes that e-commerce infrastructure is moving beyond traditional SaaS platforms to AI-enabled systems that can automate operational tasks and assist merchants in running day-to-day commerce activities.

“E-commerce is entering its deepest waters. The merchants who survive aren’t looking for more tools; they need an intelligent co-pilot,” said Jeff Li, Founder and CEO of Shoplazza. “That’s why we’re building end-to-end AI agents that don’t just execute tasks, but think, analyze, and optimize across the entire customer journey.”

SOURCE: PRNewswire

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Noibu, a leader in ecommerce site health and experience intelligence, announced its official evolution into a comprehensive Ecommerce Analytics & Monitoring Platform. This repositioning unifies Noibu’s industry-leading error monitoring with deep experience analytics, giving retailers a single, revenue-first command center to protect existing sales and proactively uncover new growth levers.

For years, Noibu has been the gold standard for identifying bugs that break checkout. But as ecommerce sites grow in complexity, reactive fixing is no longer enough. To win in 2026, retailers must eliminate the “fragmentation tax”- the hidden cost of disconnected tools for technical monitoring and behavioral analytics that fail to provide a unified path to growth.

Protecting the Floor, Raising the Ceiling

Noibu’s platform approach closes the gap between technical health and business outcomes. By connecting every technical insight directly to a dollar-value impact and real-user session context, ecommerce teams can see the complete picture: not just what is broken, but why it’s happening and how it’s gating their next million dollars in revenue.

Also Read: Syndigo Acquires Taggstar to Make Product Experiences Dynamic and Contextual

“The honest answer for why we’ve evolved is that our customers changed the game for us,” said Kailin Noivo, President & Co-founder of Noibu. “We watched our most successful users stop treating Noibu as a reactive debugging tool and start treating it as their daily ecommerce command center. We’re no longer just finding bugs; we’re uncovering revenue opportunities that traditional analytics tools simply miss.”

A Unified Platform Built on Three Core Pillars

Noibu has organized its platform into three strategic pillars designed for cross-team adoption:

• Protect Revenue & Reduce Risk: The industry’s most advanced detection of site errors, server issues, and failed releases, prioritized by actual revenue exposure so teams always fix the highest-value issues first.
• Unlock Conversion Growth: Leveraging AI Session Search, page-level diagnostics, and performance monitoring to surface the specific UI/UX levers that drive measurable lift.
• Align Teams with One Console: A single pane of glass that eliminates silos between Product, Tech, Engineering, Support, UX, and Marketing teams.

“When it comes to managing a mature digital business, the hardest part isn’t getting more data, it’s getting teams aligned on what to do next. Noibu brings monitoring and experience analytics into one revenue-focused view, which makes it easier for our teams to cut through the noise, align on priorities, and focus on the work that actually moves the needle.” Said Alexandria Sims, VP of Transformation, Sleep Country

Built for the Modern Commerce Stack

Noibu’s expanded platform is attracting strategic partners and delivering results at enterprise scale. It is now natively integrated with major commerce providers including Shopify, commercetools, and Commerce (BigCommerce) for frictionless deployment into any modern tech stack.

“Noibu started as bug detection software for us, the secret power to validate issues we couldn’t replicate. But now we see it as a partner and an ecosystem; we use it beyond bugs; layering session replay with UX insights like heat maps to understand the customer journey and improve where shoppers fall off. We see that as the biggest growth factor for Noibu.” Said Philip Krynsky, CEO & Founder, Rvinyl.

SOURCE: BusinessWire

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Syndigo, a global leader in Product Experience Management (PXM), announced it has acquired Taggstar, a leading AI-powered conversion rate optimization platform for digital commerce. The strategic investment expands Syndigo’s Product Experience Cloud to include a critical piece of the digital shopper’s journey: confidence to purchase. This enables brands and retailers to increase conversions across all digital channels, including retailer ecommerce platforms and brand-owned websites.

Brands and retailers invest heavily to ensure product data is accurate, complete, compliant, enhanced, and discoverable. Yet they have mere seconds to grab attention on their product detail pages before shoppers bounce, especially on mobile devices which represent the majority of ecommerce traffic. Taggstar powers dynamic, real-time conversion messages such as “Selling fast! 50 bought in the last hour” that engage shoppers and act as virtual sales assistants, guiding them through the buying journey with timely messages that reinforce trust and inspire action. Layered onto Syndigo’s data-rich PXM solution and combined with trusted ratings and reviews from PowerReviews, messaging insights become even more powerful, amplifying content and using contextual data to create tailored messages, improving shopping experiences, and increasing conversions.

Taggstar connects to the commerce ecosystem through a lightweight JavaScript tag, making it easy to implement on eCommerce websites and native apps with a rapid speed to value. Taggstar social proof messaging can be extended across digital channels such as email, social, website, digital ads and now PXM simply using an API. Taggstar uses AI to optimize message combinations to increase conversion rates and enrich product data, enabling merchandisers to create high-performing campaigns.

Also Read: Decagon Debuts Proactive Agents for Agentic Commerce

“With Taggstar, we’re strengthening the product and shopping experience to include real-time trends, bestsellers, reviews and shopper context, driving higher conversion,” said Simon Angove, Chief Executive Officer at Syndigo. “Their technology brings PXM to life, making otherwise static data usable across mobile, AI, and all digital shopping experiences. We are thrilled to welcome the Taggstar team, customers, and technology to Syndigo.”

Product Experience Management Now Includes Social Proof Messaging

Syndigo Product Experience Cloud powers winning product experiences for brands and retailers by:

• Creating a one-stop shop for product content
• Enriching products with structured attributes, imagery, and enhanced content
• Leveraging trusted customer ratings and reviews through PowerReviews to build credibility and shopper confidence
• Delivering channel-ready, compliant experiences optimized for retailers and marketplaces

Taggstar drives trust, action, and conversion through:

• Social Proof Messaging: Real-time social and behavioral messaging
• Attribute Messaging: Contextual reassurance and intent signals
• Dynamic Badging: Product cues embedded directly into the shopping journey

“We have over 36,000 products available online,” said Paul Canavan, Digital Director at Wickes. “Showing our digital customers tailored messaging customized to our brand, social signals, and trusted product feedback at the right moment in their journey is critical to helping them find what they need to decide and purchase quickly. Plus, Taggstar’s robust approach to measurement and performance made the ROI clear from the beginning.”

SOURCE: Syndigo

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Building on the rapid success of Adaptive Transformer Search (ATS), which virtually eliminated zero-result searches, Particular Audience now enables retailers to A/B test the AI models that determine relevance, margin, and retail media yield.

Particular Audience announced the launch of Search Model A/B Testing, a new capability that allows retailers to directly compare and govern the AI models driving on-site search and sponsored results.

This launch builds on the success of Adaptive Transformer Search (ATS), first introduced in 2023 to solve the $300bn product discovery problem. While the industry average for zero-result searches remains close to 20%, ATS clients consistently operate below 0.5%, proving that modern AI can reliably understand long-tail and conversational intent.

1% gains in relevance can mean hundreds of thousands of dollars in recovered revenue, with AI’s rapid evolution, the threat facing retailers is standing still.

Also Read: Onetag Acquires Aryel to Build an Integrated Value Exchange for Programmatic Advertising

From Finding Keywords to Capturing Intent

The majority of search technology in 2026 remains keyword-first. Even platforms claiming to use AI often rely on vectors only for early recall, before reverting to rules-based ranking. This works for simple queries, but breaks down when intent is nuanced, seasonal, or contextual.

True relevance-understanding what a customer means and selecting the right product in that moment—requires a different approach.

ATS uses a single relevance model to evaluate intent, product suitability, and commercial context (for a retailer’s KPIs) together. Search Model A/B Testing extends this foundation by unlocking componentry and allowing retailers to test different relevance strategies directly, rather than accepting a vendor’s default behavior.

For the first time the debate at a retailer shifts from “which search vendor is the best”, to “which model is best for us right now, and how might we constantly prove it?”.

At the same time, most multi-brand retail platforms still operate two separate systems: one for organic search and another for sponsored products. These systems are typically stitched together late in the process using rules or rank overrides. That approach may blend results visually, but it does not unify relevance and put the customer experience first.

Why A/B Test Search Models?

Because relevance is not static.

A retailer may want:

• Faster models for high-traffic promotional events
• Margin-weighted models during peak trading periods
• Brand-safe models for premium categories
• Multilingual models for cross-border expansion

Each of these goals involves trade-offs between speed, recall, margin, and localization. Search Model A/B Testing makes those trade-offs measurable and selectable.

Retailers can now test different model architectures side-by-side and choose the one that best aligns with their commercial strategy—using clear metrics such as conversion rate, average order value, margin contribution, and zero-result rate.

Particular Audience’s latest release allows a retailer to configure models based on unique sentence embedding structures, distinct foundational LLMs, and select reinforcement learning data—both genuine and synthetic—to tune their models. Expert customer support is 24/7 and takes the technical sting out of the tail making model manipulation just a request away.

Implications for Retail Media Networks

For Retail Media Networks, this shift only applies to the leading networks that have evolved from legacy keyword targeting approaches.

ATS turns previously unanswerable, long-tail queries into valid result pages, creating new monetizable inventory. Model-level testing then allows retailers to optimize how sponsored products are surfaced within true relevance-not as interruptions, but as contextually appropriate outcomes. Particular Audience’s Search Model A/B Testing allows yield optimization for Retail Media Networks for the very first time.

This moves retail media from placement optimization (keyword bidding) to intent-level optimization (automating high-yield targeting), improving both advertiser performance and network yield.

ATS unlocks the entire grid representing a force multiplier on sponsored search revenue.

From Black Box to Glass Box

Most vendors offer a single, opaque algorithm that decides what is relevant. Particular Audience exposes relevance as a governed decisioning layer.

“AI driven discovery is now table stakes,” said James Taylor, Founder & CEO at Particular Audience. “What matters is how relevance behaves under different business constraints, and how retailers can ensure their trust in their search model(s) is constantly positively reinforced. ATS ensures customers always find something. Model A/B Testing ensures retailers choose what kind of relevance best serves their strategy.”

About Particular Audience

Particular Audience is a retail AI company helping retailers and retail media networks compete through superior discovery, relevance, and decisioning. Its flagship product, Adaptive Transformer Search (ATS), has set a new standard for handling conversational and long-tail commerce queries, virtually eliminating zero-result searches. PA’s platform unifies search, merchandising, and retail media within a single relevance framework-giving retailers control, transparency, and measurable outcomes.

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Decagon, the leader in conversational AI agents for concierge customer experiences, introduced a new and proactive generation of its agentic technology that anticipates customer needs, remembers customer nuance, and initiates customer contact at the right time and for the right reason.

Decagon built the new capabilities alongside its own business customers in the chosen sectors of travel, retail, and healthtech- testing scenarios where the difference between Decagon’s concierge experiences and status-quo customer service tech proved to be the difference between having a smooth trip, seamless purchase, and good night’s sleep or missing a flight, package, or critical health insight.

“We have a core principle here at Decagon: invent what customers want,” said Alan Yiu, VP of Product at Decagon. “That means working closely with the companies actually deploying our agents and iterating quickly based on what we see in the real world. Our goal is to build technology that helps businesses deliver the kind of fast, concierge experiences today’s consumers deserve.”

Also Read: Opensend Acquires Fueled.io to Boost Customer Data Activation for E-Commerce Brands

So what is Decagon’s latest technology all about, especially as businesses grapple with everything from overhyped distractions to hyperscale disruption?

Here’s what you need to know:

CX is moving from automated reaction to anticipatory pro-action

Decagon’s new outbound voice capability enables voice agents to proactively call customers, with voice models fine-tuned to handle the unique technical challenges of outbound calling. As a result, teams can reach customers reliably without manual overhead.

“Decagon gave us a way to shift from reactive support to proactive outreach, while empowering our team to focus on the customer interactions where the human touch matters most,” said Vikram Rajagopalan, VP of Customer Experience at Hertz.

To date, businesses have only had the tools for table stakes work: answering customer questions faster and ultimately reducing support costs. The problem is that reactive automation, whether software- or AI-driven, still treats every interaction like a blank slate.

“A trusted concierge remembers who you are, anticipates your needs, reaches out at the right moment, and builds context over time,” said Mr. Yiu at Decagon. “With this launch, we’re helping businesses deliver this concierge treatment at global scale and empower their customer-facing teams to do their best work.”

Memory is now ‘CX infrastructure’ for real relationships

“My philosophy around customer relationships is that it’s not just about what you do for a customer, it’s about how you make them feel,” said Daryl Unger, VP of Customer Experience at Away, “As human beings, we’re wired to remember emotions far longer than we remember transactions, and that emotional impact is what ultimately drives loyalty. That’s why every interaction matters.”

Traditional systems are configured on top of static records, requiring manual maintenance and laborious management that’s impossible to do well at global scale.

Decagon’s new user memory capability serves as a new paradigm for CX infrastructure, complementing rich systems of record, capturing conversational context, preferences, sentiment signals, and behavioral patterns, and putting them to work in real-world customer interactions. And with deep security and governance standards, this personalization isn’t coming at the expense of compliance.

The result is a win-win all around:

• A customer can resume a troubleshooting thread days later without repeating themselves.
• The AI concierge agent remembers a preferred size, feature request, or product interest.
• The brand outreach is informed by past engagement, not generic segmentation.
• The CX team can see the full arc of a customer relationship in one unified view.

“Decagon allows us to retain meaningful context from past customer interactions, so we can serve customers with continuity and understanding rather than starting from scratch each time. It shifts our approach from handling isolated transactions to building relationships that evolve over years,” added Mr. Unger.

SOURCE: BusinessWire

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