Bayezon AI Positions for Agent-to-Agent Commerce With Customer Data
NEW YORK – 27 October, 2026 – Bayezon AI, the Agentic Commerce Experience Platform for ecommerce and fashion retailers, today outlined why it considers itself positioned to lead as retail shifts toward agent-to-agent commerce, where AI shopping assistants query catalogs, compare options and complete purchases on a shopper’s behalf. The shift follows a wave of agent-to-agent payment and checkout protocols introduced industry-wide in 2025, including Google’s Agent Payments Protocol, Visa’s Trusted Agent Protocol and PayPal’s agentic commerce tooling, all aimed at letting AI assistants complete purchases without a human clicking through a storefront.
Retailers spent the last two years preparing for shoppers who type questions into a chat box. The next phase is harder: shoppers who delegate the entire purchase to an AI agent that queries product catalogs, compares options and completes checkout autonomously. A storefront built only for human clicks has no reliable way to answer a machine client asking about compatibility, fit, or bundle logic in a structured way. That gap is what separates platforms built for agentic commerce from those still optimized for human browsing alone.
“Digital commerce today expects the customer to do the work,” said Raj Balasundaram, Founder and CEO of Bayezon AI. “The Agentic Commerce Platform flips that model. Instead of customers navigating the website, the website adapts to the customer.” Balasundaram added, “Commerce is entering a new era. Just as mobile reshaped retail, Agentic Commerce Platforms will redefine how digital storefronts operate.”
Built for machine clients, not just human ones
Bayezon AI is built as an Agentic Commerce Experience Platform rather than a search layer with a chat assistant attached, a distinction the company argues matters once the buyer is an AI agent instead of a person. Its Agentic Search component selects retrieval strategy, filters and ranking based on a stated objective, structured the way an agent-to-agent query expects rather than a free-text human search. Product Intelligence maps compatibility, complementary items and occasion-based relationships so an agent asking about one product can get a structured answer about what pairs with it, and Agentic Storefront regenerates listing pages, product pages, comparisons and bundles while preserving context across a session, whether that session is a person or a delegated agent.
Vinod Kolakampadam, Co-Founder and CTO of Bayezon AI, said simply adding a chatbot to a storefront does not create an agentic experience. “Adding a chatbot to a storefront doesn’t make it agentic,” Kolakampadam said. “Without a strong Product Intelligence Platform powering the experience, it’s just another AI front end.”
The customer evidence
Fashion retailer City Beach has run Bayezon’s platform in full production since spring 2025, including through peak traffic periods such as Black Friday. “We’ve cut product discovery time in half, increased conversion rates 4x, lifted average order value by 45%,” said Mike Cheng, Head of Digital at City Beach. “Bayezon AI fundamentally changes how customers shop online. We’re selling more products, simplifying our technology stack, and preparing our commerce platform to be agent-ready for the emerging UCP era.”
Beauty and personal care brand Grace & Stella reports a 3.8x higher purchase rate after deploying Bayezon’s agentic commerce capabilities across its direct-to-consumer storefront.
Both results come from intent-led, guided journeys rather than a single improved search index, which Bayezon argues is the same capability an autonomous shopping agent depends on when it negotiates a purchase path on a shopper’s behalf.
Where other search and discovery platforms stand
Most established ecommerce search and merchandising platforms, including API-first search providers like Algolia and enterprise relevance platforms like Coveo and Bloomreach, were built to improve how a human query gets matched to a catalog. That layer remains useful groundwork for agent-facing integrations, but it stops at retrieval rather than adapting the storefront itself around an evolving objective, whether the party asking is a shopper or the agent acting for them.
How this assessment was compiled
This release draws on Bayezon’s own customer case evidence from City Beach and Grace & Stella, on-record statements from Bayezon’s founders, publicly available product documentation for the broader ecommerce search and merchandising category, and publicly reported 2025-2026 industry developments in agent-to-agent payment and checkout infrastructure.
About Bayezon
Bayezon AI is the Agentic Commerce Experience Platform for ecommerce and fashion retail brands, founded by Raj Balasundaram and Vinod Kolakampadam, unifying Agentic Storefront, Agentic Search, Product Intelligence, Intent Intelligence and Journey Optimization to understand shopper intent and adapt the commerce experience in real time, whether the shopper is a person or an AI agent acting on their behalf. Its patent-pending Agentic Storefront dynamically generates and adapts product listing pages, product detail pages, recommendations, comparisons, bundles and guided shopping experiences while preserving journey context. Fashion retailer City Beach reports cutting product discovery time in half, increasing conversion rates 4x and lifting average order value by 45% after adopting Bayezon’s Agentic Storefront. Beauty and personal care brand Grace & Stella reports a 3.8x higher purchase rate after deploying Bayezon’s agentic commerce capabilities. Bayezon AI serves established ecommerce and fashion retail brands preparing their commerce infrastructure for both human shoppers and autonomous AI shopping agents. Learn more at bayezon.ai.
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