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OpenAI Retail North America

Lowe’s

Customer Service Automation

Lowe’s needed to make project expertise available to both customers and associates across huge stores and digital channels, where traditional search and department knowledge were not enough to guide complex home-improvement decisions.

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Customer satisfaction increased by 200 basis points when associates use Mylow Companion
Mylow and Mylow Companion have answered millions of questions since launch
Mylow users convert at three times the rate of online shoppers who do not use Mylow
Nearly half of Mylow Companion interactions happen through voice

Reported impact

Business outcome

Lowe’s answered millions of project questions, increased online conversion and in-store satisfaction, and made expertise easier to access for both new customers and front-line associates at national retail scale.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

Lowe’s built two OpenAI-powered assistants around a project-first retail model instead of a product-first search model. Mylow Companion supports store associates on mobile and desktop, answering conversational questions across departments and often by voice so staff can stay with shoppers in the aisle. Consumer-facing Mylow brings the same style of help to Lowes.com, where customers can describe their project in natural language and receive step-by-step guidance, product recommendations, and related how-to content instead of relying on exact keywords. Both assistants are grounded in Lowe’s project knowledge and designed to reason through real home-improvement scenarios, from paint selection to repair work, at national retail scale. Lowe’s has also extended OpenAI into its Store Support Center with ChatGPT Enterprise and continues to deepen voice interaction so millions of everyday project questions can be answered with more confidence and less friction.

Data flow

Customer and associate project questions are routed into Mylow or Mylow Companion, where OpenAI models reason over Lowe’s project knowledge and return guidance, recommendations, and how-to content. Associates and customers then act on those answers online or in-store, while support-center workflows extend the same model family into enterprise operations.

Solution architecture

3 components · 1 layer
  1. Serving
    • Mylow Provides conversational project guidance, recommendations, and how-to content for customers on Lowes.com.
    • Mylow Companion Delivers conversational project expertise to store associates across departments, often through voice interaction.
    • ChatGPT Enterprise Extends OpenAI usage into Lowe’s Store Support Center and broader enterprise workflows.

Architecture clues

  • ChatGPT Enterprise was later adopted in the Store Support Center
  • Mylow Companion is available on mobile and desktop in stores
  • Project-first architecture is designed around natural-language questions instead of product keywords
  • Voice interaction is central for associates on the move

Evidence from the source

"Built with OpenAI, Mylow and Mylow Companion ... have helped Lowe’s answer millions of questions"
"Mylow Companion is available to roughly 300,000 associates across 1,700 stores"
"Mylow users convert at three times the rate of online shoppers who do not use Mylow"
"Nearly half of Mylow Companion interactions happen through voice"
Transparency Evidence & methodology 90/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. OpenAI

    Lowe’s customer story

    First-party
    https://openai.com/index/lowes/
    Source type
    Vendor-published customer story
    Published
    Sep 25, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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