Home Insights Case Studies GenAI-powered search for 40M+ auto parts on WhatsApp: A case study

GenAI-powered search for 40M+ auto parts on WhatsApp: A case study

Magnifying glass overlaying collaged ocean and abstract imagery representing AI-powered WhatsApp search technology.

Transform automotive customer engagement with AI search agents

Delivering customer service at the speed of today’s expectations is not optional anymore. See how a leading automotive retailer restructured its e-commerce search and customer support by implementing a conversational AI agent on WhatsApp and achieved response times as fast as 3 seconds, enabling round-the-clock product search, fitment checks, and order placement. 

Download the full case study for architecture diagrams, implementation methodology, and technical specifications.

Why AI-powered search and support?

Traditional support channels encounter challenges such as high volumes, slow response times, and customer dissatisfaction. However, with conversational AI-powered agents, every product inquiry and support request is handled instantly, accurately, and at scale. AI search and conversational AI together deliver a seamless experience, allowing users to discover and purchase products through their preferred channel whenever they need them. 

AI for customer service that scales 

When customers couldn’t find the right parts quickly, and systems couldn’t scale to meet the rising demand, Grid Dynamics delivered a solution that transformed search and engagement. The product catalog was modernized with ACES/PIES standards for seamless vendor integration, eliminating manual fitment maintenance and increasing catalog capacity by 250%, from 14M to 40M products.

GenAI search on WhatsApp: Search, ask, buy

The foundation enabled a breakthrough GenAI-powered WhatsApp solution, allowing customers to search for auto parts using text, images, or voice messages and complete purchases without leaving the app. 

Powered by LangGraph and Gemini 2.5, the AI WhatsApp AI agent delivers: 

  • Visual search via image recognition
  • Voice message support for hands-free interaction
  • Real-time access to 500,000+ SKUs with pricing and availability 
  • Dynamic vehicle questionnaires for accurate fitment
  • Order placement in 3-5 seconds (95% faster than manual)

Measurable business impact 

  • 24/7 automated support across English, Spanish, and Portuguese
  • 1000+ stores, scalable conversation volume
  • 6,700+ locations unified under one search architecture
  • B2B & B2C customers served through omnichannel integration

Store managers are now freed from constant message monitoring, while analytics provide visibility into demand by store, hub, and region. 

Trusted by industry leaders

“Grid Dynamics has been an invaluable partner… Their expertise and innovation make them a trusted partner in our AI journey.” —Sr. Engineering Director

Recognized as Supplier of the Year at the 2025 IT Supplier Summit.

See how AI is redefining customer service in retail 

Learn how scalable, AI-powered MXP, conversational customer engagement, and AI search redefine operational efficiency and customer loyalty. See the full journey from challenge to transformation, with concrete results and explore the architecture, technologies, and implementation plan behind this AI success story.

Authors

Tags

You might also like

Building a global technical support team in 30 days case study cover
Case Study
Building a global technical support team in 30 days
Case Study Building a global technical support team in 30 days

With consistent global user growth reaching 15M+ active users, a leading software development tools company needed a reliable partner to quickly onboard and manage skilled support teams across multiple countries and time zones.​ 

Modernizing a legacy commerce stack under pressure case studies cover
Case Study
Modernizing a legacy commerce stack under pressure
Case Study Modernizing a legacy commerce stack under pressure

A distinguished British luxury jeweler partnered with us to replace a decade-old SAP Hybris platform with a modern, composable architecture featuring 15 external integrations, all while existing ecommerce trade continued without interruption. The goal was to deliver a faster, more flexible omnichann...

Abstract illustration of humanoid figures using laptops, with a case study cover titled “AI observability for a retail sales enablement assistant.”
Case Study
AI agent observability catches mistakes before they hit the sales floor
Case Study AI agent observability catches mistakes before they hit the sales floor

A leading sleep solutions retailer built an AI sales enablement assistant to give more than 6,000 store associates instant access to product knowledge, promotions, and coaching. Adoption grew fast. Visibility into production performance did not keep pace. Golden datasets and manual log reviews were...

Brightly colored red and purple Galeries Lafayette store interior dome as the cover image for Search and Merchandising case study
Case Study
Galeries Lafayette success story: 7% revenue lift with AI search and merchandising
Case Study Galeries Lafayette success story: 7% revenue lift with AI search and merchandising

Galeries Lafayette operates 335 stores and a fast-growing e-commerce business that attracts more than 60 million visitors annually. As demand for hyper-personalized shopping experiences increased, the company needed to modernize its search and merchandising capabilities to improve relevance, increa...

Cover of a Case Study with macro close-up of dark blue denim fabric with texture and copper-colored stitching details on it
Case Study
How G-Star accelerated product onboarding from weeks to days with AI catalog enrichment
Case Study How G-Star accelerated product onboarding from weeks to days with AI catalog enrichment

As customer expectations shift toward more personalized, content-rich experiences, the pressure to scale product content continues to grow. Recognizing this, G-Star, a digital leader in fashion, set out to modernize its catalog operations and improve how product content is created and delivered...

Grid Dynamics case study cover on AI-powered legacy modernization for a healthcare revenue SaaS platform, featuring abstract medical imagery.
Case Study
Accelerating legacy modernization with an AI-powered SDLC
Case Study Accelerating legacy modernization with an AI-powered SDLC

A leading healthcare revenue cycle management (RCM) SaaS platform provider faced a fixed deadline to migrate its on-premise, monolithic claims platform. Rapid growth and multiple acquisitions had compounded legacy debt, making the .NET 4.5 application difficult to scale and maintain. The company ne...

Virtual agent against pink, yellow and blue background with a credit card overlay to represent multi-agent enterprise workflows for payments leader
Case Study
Multi-agent enterprise transformation for Fortune 500 payments leader
Case Study Multi-agent enterprise transformation for Fortune 500 payments leader

A leading global payments technology company needed a practical way to deploy AI agents across regulated business domains without sacrificing control, transparency, or reliability. The organization partnered with Grid Dynamics to implement a production-safe agentic AI platform that standardizes how...

Let's talk

    This field is required.
    This field is required.
    This field is required.
    By sharing, I consent to the use or processing of my personal information by Grid Dynamics for the purpose of fulfilling this request and in accordance with Grid Dynamics’s Privacy Policy. For more details about how to opt-out, please refer to the Privacy Policy and Terms & Conditions.
    Submitting

    Industry Recognition

    MACH, Agent Ready 2026 Award

    2026

    MACH Alliance Awards, Best Retail Project

    2024, 2025

    AutoZone, IT Supplier of the Year

    2025, 2026

    Sysco, Technology Partner of the Year

    2026
    Abstract glass goblet sculpture

    Thank you!

    It is very important to be in touch with you.
    We will get back to you soon. Have a great day!

    check

    Thank you for reaching out!

    We value your time and our team will be in touch soon.

    check

    Something went wrong...

    There are possible difficulties with connection or other issues.
    Please try again after some time.

    Retry