Gala Pustova is CEO and co-founder of Catomize, where Pustova works on real-time e-commerce data infrastructure and its role in storefront performance and agentic commerce.
Useful for explaining how e-commerce infrastructure problems affect sales and AI agent shopping.
Best for
B2B commerce infrastructure or retail tech thought leadership for technical retail leaders.
Not for
Consumer lifestyle endorsements, mass reach, demographic targeting, or guaranteed sales campaigns.
Key takeaways
An audit found category pages took nearly six seconds to load.
Inventory data was as much as 24 hours out of date.
Skeleton screens can reassure human shoppers but leave AI agents no usable content.
Check first: Ask for independent validation of Catomize audit performance and sales risk claims.
Main ideas
The direct economic cost of stale e-commerce data · AI agents demand radical storefront architectural changes · Evolving infrastructure for Agentic Commerce
Gala Pustova is CEO and co-founder of Catomize, which focuses on real-time e-commerce data infrastructure.
They connect familiar retailer problems—slow pages and out-of-date stock or pricing—to their commercial costs, then explore how those problems matter when AI agents shop on customers’ behalf.
Their posts offer a practical perspective on agentic commerce, grounded in retailer examples and technical proposals.
Pustova makes infrastructure issues tangible by tracing them to business consequences.
In one post, they describe an audit where category pages took nearly six seconds to load and inventory data was as much as 24 hours out of date, with estimated sales at risk.
In another, they explain why a skeleton screen that reassures a human shopper can leave an AI agent with no usable page content, and point to machine-readable product data and server-rendered pages as possible responses.
Galyna Pustova argues that modern e-commerce depends on real-time data infrastructure and must evolve to serve automated AI agents.
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They say traditional storefront practices are often insufficient or actively harmful to AI-driven commerce, causing significant, often overlooked financial losses for retailers throughout the year. Their focus is on the architectural shifts needed to maintain extreme data freshness and speed as AI mediates purchasing decisions.
The direct economic cost of stale e-commerce data
Pustova argues that e-commerce businesses lose substantial revenue when product data, pricing, and inventory updates lag between backend systems and the customer storefront.
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They say these infrastructure problems are not cyclical and often expose systemic weaknesses year-round.
In one audit, a retailer’s category page took six seconds to load while stock synchronization lagged by up to 24 hours, putting an estimated €220k to €300k in annual revenue at risk for a business generating €1.1M.
The problem has evolved from basic synchronization issues to comprehensive, real-time data infrastructure.
AI agents demand radical storefront architectural changes
Pustova challenges the assumption that e-commerce traffic is solely human-led.
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They argue that AI agents operate with strict time and step budgets and are likely to abandon storefronts that are poorly structured or too complex for machines to parse.
Pustova says 'skeleton screens'—a standard practice for human perception—are just pages without content to an agent, making standard fixes inadequate. Instead, pages must be served as server-side HTML, assembled in under 100 milliseconds, with structured data protocols such as JSON-LD.
Pustova has shifted from describing agent behavior to showing how to build systems that accommodate it, including Catomize's server-side rendering approach.
As AI agents become key interfaces for commerce, Pustova sees data infrastructure as the crucial layer connecting human and non-human buyers.
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That shift requires moving beyond simple data exchange to building real-time, high-throughput data ecosystems.
Catomize has expanded its integration layer to include native AWS and Google Cloud integrations and developed modules for real-time data synchronization, giving AI agents immediate access to accurate product and price data.
The urgency of AI-driven commerce has accelerated development of the company’s MCP module and strategic partnerships with companies like Hyvä.
The necessity of end-to-end performance evaluation
Pustova says technical performance must be measured across both front-end speed and back-end efficiency, particularly as AI agents increase demand for instant data.
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Technical benchmarking showed product-page server response time fall from 107 ms to 11.6 ms, alongside significantly higher server throughput on identical infrastructure.
These technical findings are increasingly tied to strategic business growth, showing how architectural advantages can translate into market relevance in the AI era.
Newcomers can use this post to understand the immediate financial risks retailers face from inefficient infrastructure.
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It moves the discussion beyond technology theory to demonstrable revenue loss.
Pustova shares findings from infrastructure audits, including revenue losses of €250,000 on €1 million in annual revenue caused by slow category-page load times and inventory data lags of up to 24 hours. They argue that events like Black Friday don't create these problems but expose them, especially as AI agents play a growing role in purchasing decisions.
For brands planning a campaign
B2B marketers can use this data to build urgent case studies or sales enablement materials, quantifying revenue lost to outdated infrastructure against the cost of modernization.
This post examines a disruptive shift: AI agents increasingly act as primary customers before human interaction.
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It shows why traditional retail assumptions are becoming liabilities.
Pustova challenges the idea that e-commerce traffic is solely human-driven.
They explain that AI agents operate under strict time and budget constraints and will give up if a storefront is complex or requires multiple steps to find information.
Retailers must rethink their architecture for conversion and visibility.
For brands planning a campaign
Brands targeting large retailers can emphasize solutions for non-human traffic, helping businesses optimize their architecture for automated purchasing behavior.
For technical buyers, this post explains the components of modern e-commerce architecture and the features needed to handle high-speed AI traffic.
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Pustova argues that standard frontend practices, such as skeleton screens and missing canonical headers, can hinder AI agents.
They detail Catomize's shift to serving pages entirely as server-side HTML, assembled from projections written at change time.
The result is structured content that loads in under 100 milliseconds and is never stale.
For brands planning a campaign
Developers and CTOs can use specific technical requirements, such as JSON-LD usage and server-side assembly, to prioritize platform development roadmaps for agentic readiness.
This overview traces Catomize's evolution from simple data synchronization to real-time data infrastructure for people and AI agents.
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Pustova outlines Catomize's strategic shift from a standalone synchronization solution to a broader ecosystem, integrating AWS and Google Cloud and developing an MCP module. The shift is driven by the need for instantaneous product, price, and availability data to support AI commerce.
For brands planning a campaign
This framework helps position complex B2B solutions by showing how platform scalability and integration depth translate into commercial opportunities and greater client value.
For audiences interested in how technical solutions work, this post highlights the practical application of high-performance e-commerce strategies and the partnerships needed for real-time data handling.
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Pustova discusses a technical conversation with co-founder Tim Bezhashvili and Tom Ketels from Hyvä about maintaining storefront speed and data freshness as AI-driven traffic grows. The discussion highlights the need to collaborate with specialized technical leaders to achieve real-time performance.
For brands planning a campaign
Marketing teams can use this collaborative approach to position the creator as a facilitator of specialized, high-level technical discussions—not just a product vendor.
Pustova argues that retailers lose sales when product, price, and stock information moves too slowly between systems and storefronts.
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They use audit examples, including inventory delays of up to 24 hours and category pages taking nearly six seconds to load, to make the problem concrete.
Connects infrastructure problems to specific operational measures and estimated revenue at risk.
For brands planning a campaign
A collaboration could help retailers assess the business cost of stale data and explain where faster synchronization may matter. Use a retailer’s own figures rather than presenting the post’s audit estimates as universal benchmarks.
Pustova’s posts frame performance as an infrastructure and rendering issue, not just a matter of interface polish.
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One describes server-rendered pages assembled from projections written when data changes; another reports a Catomize comparison showing a lower server response time and fewer database queries on identical infrastructure.
Explains technical choices through before-and-after measurements and contrasts relatively straightforward fixes with changes that require architectural work.
For brands planning a campaign
This subject suits technical thought leadership or an advisory discussion for e-commerce teams weighing storefront architecture, data freshness, and operating costs. Present any performance figures as results from the described comparison, not as a general guarantee.
Pustova describes agents as visitors with limited time and steps, unlike human shoppers who may wait or recover from a confusing page.
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They argue that stale data, empty listings, inaccessible images, and unstable page structures can hinder agent-led product discovery and purchasing.
Uses a human-versus-agent comparison, then separates lower-effort discoverability improvements from deeper rendering and architecture changes.
For brands planning a campaign
A campaign could explore how retailers can make product information and storefront pages usable by both people and automated agents. The posts offer practical technical examples.
Pustova argues that infrastructure weaknesses create losses throughout the year and that high-traffic periods expose existing problems rather than cause them.
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They also connect always-on AI-agent activity with pressure beyond traditional retail calendars, citing examples of estimated lost sales and dead stock.
For brands planning a campaign
This topic could support a retailer-focused discussion about year-round readiness, inventory accuracy, and the commercial effects of slow storefronts. Before using the audit estimates, ask for the methodology and supporting data.
Pustova’s proposed conference talk asks what happens when retailers no longer own the customer journey.
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They link agent-led comparison and purchasing decisions to customer acquisition, conversion, visibility, and revenue, extending the infrastructure discussion into commercial strategy.
For brands planning a campaign
This is a potential thought-leadership angle for brands addressing how AI-mediated discovery may affect retail relationships with customers. Ask whether the proposed talk has since been delivered and what results it produced.
Pustova's LinkedIn posts make a consistent case: slow or stale product, price and stock data costs retailers money, and AI agents make that cost harder to ignore.
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One post traces Catomize's year of change alongside two retail audits: a category page loading in about six seconds, stock synchronisation running up to 24 hours behind, and an estimated €220k–€300k a year at risk for a business with roughly €1.1M in revenue.
A companion post explains how agents differ from human shoppers, separating cheap fixes—machine-readable entry points and JSON-LD for Product, Offer, BreadcrumbList and FAQPage—from rendering changes that call for a rebuild.
A post announcing their Future Commerce article and two conference posts broaden the focus from storefront mechanics to year-round lost sales, dead stock and who owns the customer journey once agents compare and buy.
An event post on keeping storefronts fast while stock and prices stay real-time shows the platform ecosystem around this work.
Van Camp treats agentic commerce as an operating challenge for merchants: whether AI platforms can read, compare and recommend a catalogue, and what enterprises must change to be found and transact through agents.
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These posts focus on platform launches and research: an Agentic Activation rollout, joint research with Merkle, a product-page readiness scan and market-growth statistics. They assess readiness through scan scores and market figures, not storefront rendering work or infrastructure audits.
Gai covers the shift from the shopper's side, asking how people and their agents will discover products and how payment and discovery layers must adapt.
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Gai ranges across the consumer and industry landscape, covering agent payment protocols from Google, Mastercard, Visa and PayPal, as well as consumer behaviour such as asking a chatbot for skincare advice. The focus is broader than one vendor's data infrastructure and measured page performance.
Miller writes for e-commerce operators about how AI-mediated shopping affects the way a store presents and maintains its catalogue, including cases where an agent must choose one vendor among identical listings.
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Miller's posts target independent stores reselling other manufacturers' products: how to stand out when a product page is not yours alone, and how to trim a catalogue to what can be maintained. Pustova's material focuses on high-throughput data infrastructure and rendering for larger retail operations.
Gala Pustova's content is rooted in B2B retail technology, focusing on the architectural challenges of modern e-commerce.
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The technical discussions target professionals responsible for e-commerce infrastructure, digital transformation and platform performance.
Key themes include real-time data requirements for AI-driven commerce, the impact of agentic bots on storefront design, inventory synchronization and database query optimization in high-traffic retail environments.
Buyers exploring technological advances in retail tech or advising clients on next-generation e-commerce infrastructure will find the material relevant.
The material does not detail official partnership formats or sponsorship packages for Gala Pustova.
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Public coverage focuses on Catomize’s technical evolution, agentic commerce, and industry speaking engagements. Ask for the available partnership options and inquiry route.
A good fit for a technically grounded thought-leadership campaign, subject to confirming the format and terms directly. Pustova’s posts connect agent access to concrete storefront issues: stale inventory, slow or empty listings, inaccessible product images, and unstable page structures. One post distinguishes relatively straightforward improvements, such as structured product data, from deeper rendering and architecture work. Another describes audit findings and a Catomize comparison, giving the campaign useful examples without relying on generic predictions about AI.
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Brief
A B2B commerce-infrastructure company wants to reach retail technology leaders with a thought-leadership collaboration about preparing storefronts for AI agents.
Angle
Show how a retailer can test whether its product pages work for shoppers and automated agents. Use a technical discussion or case-based article to walk through a small set of checks, then explain which findings call for quick fixes and which require architectural work. Label any performance or revenue figures as specific examples from Pustova’s posts, not general benchmarks.
When to choose a specialist
Poor fit based on Pustova’s posts, which focus on B2B retail infrastructure, data synchronization, storefront performance, and agentic commerce rather than consumer lifestyle content or fashion recommendations. The posts offer no evidence for the requested mass reach, demographic targeting, sales guarantees, or ready-made endorsement package. A buyer pursuing consumer product promotion should seek a creator whose work and audience data directly match that goal.
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Brief
A consumer fashion brand wants a mass-reach lifestyle endorsement with guaranteed sales, broad demographic targeting, and a fixed sponsored-post package.
A worked campaign example
Next step
Ask Pustova whether they are available for a technical thought-leadership collaboration, what formats they accept, and whether they can provide suitable, approved case evidence. Separately agree on the retailer’s baseline measures and permissions before drafting claims.
Why this fits
This builds on Pustova’s comparison of human and agent experiences and their distinction between finite technical fixes and architectural problems. Their posts also connect data freshness and page speed to commercial risk, making the subject relevant to technical and business stakeholders. Attribute any Catomize performance figures or audit estimates to the specific examples described in the posts, not as guaranteed outcomes for the campaign partner.
Brief
A retail platform provider wants a practical campaign for e-commerce engineering and operations leaders assessing agent readiness.
Campaign angle
“Can an AI agent actually shop your storefront?” Use a retailer’s product listing as the working example. First check whether the page returns usable product, price, and availability information; then check whether images and page structure are accessible and stable. Separate quick discoverability work, such as structured product data, from architecture changes needed when listings render empty or data is stale. Close with a short diagnostic checklist and a discussion of what the retailer should measure next.
Recommendation
Explore a co-developed technical discussion or case-based article with Pustova, after agreeing on the format and checking which client examples and claims can be shared.
Choose Amelia Van Camp when the campaign needs a marketplace-platform view of agent readiness aimed at enterprise merchants, rather than a storefront-architecture view.
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Her posts describe Mirakl's readiness analyzer, which scanned product pages and found under 1% scoring above the level where AI agents reliably surface products, and Mirakl Agentic Activation, which enriches product content and connects merchants to LLM platforms, starting with Microsoft Copilot.
A second post covers a joint Mirakl and Merkle playbook on enterprise readiness, citing AI referral traffic growth and marketplace GMV growth.
That framing suits a campaign about discovery and recommendation at catalog scale across many merchants.
The material draws on Mirakl's own scan, Salesforce and market research, not audits of a named storefront.
A campaign built on these posts therefore inherits the platform vendor's framing and relies partly on data the vendor did not collect itself.
Ask for independent merchant-outcome evidence and examples of rendering, page speed or data freshness before making performance claims.
Choose Sharon Gai when the audience is consumer-facing or focused on checkout and payment.
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One post maps four agent payment schemes — Google's Agent Payments Protocol (AP2, backed by PayPal, AmEx and Coinbase), Mastercard Agent Pay, Visa's Trusted Agent Protocol and PayPal's Agentic Commerce Platform — and explains why merchants will need to be readable and transactable by agents.
The other opens with shoppers asking ChatGPT for skincare advice and traces the move from marketplace listings to social commerce to agentic commerce, noting that answer-engine traffic already reaches roughly 10% for some sites.
This suits a campaign about how agent purchases are authorized and paid for, and how shopper behaviour is changing.
Both posts frame longer articles; the promised explanation of how the layers of agentic commerce work is not in the posts themselves.
The coverage maps the ecosystem and offers cultural commentary, without storefront audits, performance measurements or infrastructure comparisons.
Payment-protocol detail changes quickly, so ask for an update before reusing the framing in a collaboration.
Choose Rob Miller when the campaign targets independent or reselling stores and needs an argument about competitive position rather than infrastructure.
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One post explains that when an AI shopping agent evaluates identical listings from several vendors, it falls back on return policy, shipping reliability, review volume, price and brand recognition, where large retailers win by default.
Independent stores must differentiate through bundled expertise, clearer service terms, specialization depth or content that helps someone choose.
The other argues for ranking a catalog by contribution rather than volume and cutting products that consume attention without earning.
This suits a campaign about how merchants compete when they do not exclusively control the product page.
These posts make a strategic, qualitative case, without rendering, data-freshness or page-speed figures or a platform benchmark.
Ask for measured diagnostic material elsewhere if the campaign requires it.
The posts address merchants deciding how to compete, rather than technology leaders buying infrastructure, so the fit depends on whether the campaign targets store operators.
The profile was built using a review of five LinkedIn posts from Pustova's account.
The content was analyzed to extract insights regarding Catomize's product evolution, technical case studies (such as audit findings related to page load times and inventory lag), and strategic commentary on the impact of AI agents on the retail landscape.
The review focuses on presented technical solutions and business implications.
