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OpenAI Retail Asia-Pacific & Oceania

Cars24

Customer Service Automation · IT Operations Automation · Sales Automation

Cars24 needed to manage a fragmented, conversation-heavy vehicle marketplace and its internal workstreams without scaling support, sales, and engineering headcount linearly with customer growth.

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12% of previously lost seller leads recovered
1M+ monthly conversation minutes handled by AI agents
50% increase in customer support resolution rates
80% reduction in turnaround time across key service workflows

Reported impact

Business outcome

Cars24 increased support resolution, recovered previously lost seller demand, reduced turnaround times, and spread AI-assisted execution from customer operations into software delivery and business operations across the company.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

Cars24 built OpenAI-powered voice and chat agents to cover the full buyer and seller journey across its automotive marketplace. Buyer-side agents qualify needs, recommend cars from the catalog, book test drives, support financing conversations, and help with after-sales questions. Seller-side agents collect vehicle details, schedule inspections, and re-engage leads that previously fell out of the funnel after 10 days. At the same time, the company rolled out ChatGPT Enterprise and Codex to roughly 600 employees and reoriented project management around Linear so Codex could participate directly in ticket creation, bug handling, work summaries, and cross-team updates. Teams beyond engineering now use Codex to automate workflows in finance, procurement, and internal communication, including chief-of-staff agents that connect Slack, Gmail, WhatsApp, and other systems. Together, these layers make AI both the customer-conversation engine and the internal operating layer for Cars24.

Data flow

Customer calls, chats, catalog data, financing details, and follow-up signals are processed by Cars24's conversational agents to move buyers and sellers through the marketplace. Internally, tickets, financial records, procurement requests, and communication streams flow through ChatGPT Enterprise, Codex, and chief-of-staff agents to automate daily work.

Solution architecture

5 components · 3 layers
  1. Compute
    • OpenAI APIs Power the conversational agents used across buying, selling, financing, and support workflows.
  2. Serving
    • AI-powered voice and chat agents Handle customer qualification, follow-up, re-engagement, support, and post-sale service.
    • ChatGPT Enterprise Supports AI workflow creation across engineering, finance, legal, marketing, and operations.
  3. Orchestration
    • Codex Participates in the software-development lifecycle and internal automation work.
    • chief of staff agents Coordinate communication, scheduling, hiring workflows, and follow-ups across internal systems.

Architecture clues

  • Linear was adopted to create a cleaner path for Codex to participate in day-to-day work
  • Non-engineering teams build internal agents that connect Slack, Gmail, WhatsApp, and other systems
  • Voice and chat agents are used across the entire buying, selling, financing, and support journey

Evidence from the source

"12% previously lost seller leads recovered"
"50% increase in customer support resolution rates"
"85% to 90% daily active usage"
"Today, we handle over a million conversation minutes a month through AI"
Transparency Evidence & methodology 100/100 reference value · 89% 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 value100/100
  • 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 impact25/25
    5 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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 provenance10/10
    The publication date comes from the source page or publisher.
  • 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

    Cars24 customer story

    First-party
    https://openai.com/index/cars24/
    Source type
    Vendor-published customer story
    Published
    Jul 16, 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

  • Published on source page Jul 16, 2026 · OpenAI · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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