quote We’ve entered a new era of conversational product search, where customer interactions with search engines drive e-commerce outcomes. At Grid Dynamics, we’re leading this shift by actively developing solutions that leverage Vertex AI Search for Retail, helping retailers unify traditional search bars and chatbot interfaces into a powerful search experience that answers abstract queries and solves customers’ challenges. The journey from keywords to conversations is well underway, and those who adapt will lead the race. Dr. Eugene Steinberg Founding engineer and VP of Technology

Explore the evolution from keyword search to natural language and now conversational search, a shift fueled by technological innovations like semantic vectors, large language models (LLM), and vector search engines, marking a major leap in e-commerce product discovery.


Learn how Google’s Vertex AI Search for Retail uses three AI-driven capabilities as its secret sauce—catalog enrichment, smart matching with Google Shopping data, and conversion-focused ranking—to guide customers to precisely what they need by asking the right questions and presenting the most relevant products.


The outcome? When a search engine effectively uses rich product data to match and rank products well, customers find what they need faster, leading to higher conversion rates, increased average order value, and stronger customer loyalty.

The conversational search secret sauce

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Generate rich catalogs using multimodal foundation models to analyze and harmonize product data from user manuals, descriptions, customer reviews, user-generated content, and images, ensuring accuracy and adherence to company photo standards—eliminating the need for marketplace sellers to manage tedious cataloging tasks.

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Match products to customer intent

Show the right products in response to a user’s query by leveraging extensive customer behavior data from Google Shopping to match products in the catalog with the customer’s shopping intent.

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Rank products by conversion likelihood

Rank all matching products using Google’s ranking algorithm, which predicts conversion propensity by analyzing product information and on-site customer behavior, ensuring that the matched products are optimized to meet the customer’s intent.

5 key takeaways

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1. Enable dynamic product comparison

Enable dynamic product comparison through a side-by-side comparison table with key product features, making it easier for customers to make informed decisions.

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2. Integrate conversational search and recommendations

Integrate conversational AI search with a recommendation engine to suggest similar products when the requested ones aren’t available.

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3. Ingest enterprise data for real-time retrieval

Ingest enterprise data directly into the search system, retrieving relevant chunks in real time to guide large language models in accurately answering queries for niche products.

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4. Ensure meaningful interactions with customer data

Ensure meaningful interactions by using customer engagement data and historical site filters to ask relevant questions for specific queries, avoiding endless chat loops that derail the shopping journey.

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5. Augment search results with AI and merchandising

Augment search results by balancing AI with traditional merchandising, and maintain appropriate behavior and accuracy with guardrails and grounding.

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About the author

Dr. Eugene Steinberg

FOUNDING ENGINEER AND VP OF TECHNOLOGY

Dr. Eugene Steinberg, a Founding engineer at Grid Dynamics and VP of Technology, leads key technology practices in commerce, search, and AI.

As a Principal Architect, Dr. Steinberg has guided numerous significant programs from inception to production. His ability to align business requirements with technological capabilities has been crucial in delivering effective solutions…

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