# Gala Pustova

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.

- Profile: https://b2binfluencer.marketing/creators/gala-pustova/
- LinkedIn: https://www.linkedin.com/in/galyna-pustova
- Topics: artificial-intelligence, ecommerce, agentic-commerce, retail-technology
- Based on 11 dated posts, latest 2026-10-07

## Summary

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.
- Check first: Ask for independent validation of Catomize audit performance and sales risk claims.

### 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- Inventory data was as much as 24 hours out of date.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.

## Main ideas

- **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. They say these infrastructure problems are not cyclical and often expose systemic weaknesses year-round.
- **AI agents demand radical storefront architectural changes**: Pustova challenges the assumption that e-commerce traffic is solely human-led. 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.
- **Evolving infrastructure for Agentic Commerce**: As AI agents become key interfaces for commerce, Pustova sees data infrastructure as the crucial layer connecting human and non-human buyers. That shift requires moving beyond simple data exchange to building real-time, high-throughput data ecosystems.
- **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.

## Work to read

- [The Economic Cost of Stale E-commerce Data](https://www.linkedin.com/posts/galyna-pustova_futurecommerce-retailtech-ecommerce-activity-7512839011850280960-xIln)
- [Agentic Commerce and the Future of Customer Acquisition](https://www.linkedin.com/posts/galyna-pustova_agenticcommerce-ecommerce-aicommerce-activity-7505189201642999808-XrSS)
- [Technical Architecture for AI-Resistant Storefront Performance](https://www.linkedin.com/posts/galyna-pustova_ecommerceberlinexpo-catomize-agenticcommerce-activity-7508824502709985280-09HF)
- [Evolving Beyond Traditional E-commerce Sync: Real-Time Data Infrastructure](https://www.linkedin.com/posts/galyna-pustova_agenticcommerce-ecommerce-infrastructure-activity-7503750939825225728-opf6)
- [Deep Dive into Real-Time Performance with Technical Partners](https://www.linkedin.com/posts/galyna-pustova_ecommerce-magento-hyva-activity-7503019498451427329-T_IW)

## Brand relationships

- **Catomize** (employment)
- **Hyvä** (disclosed_partnership)

## Partnerships

The material does not detail official partnership formats or sponsorship packages for Gala Pustova. Public coverage focuses on Catomize’s technical evolution, agentic commerce, and industry speaking engagements. Ask for the available partnership options and inquiry route.

## Alternatives

- Amelia Van Camp: 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. 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.
- Sharon Gai: Choose Sharon Gai when the audience is consumer-facing or focused on checkout and payment. 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.
- Rob Miller: Choose Rob Miller when the campaign targets independent or reselling stores and needs an argument about competitive position rather than infrastructure. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/