Google Cloud Retail New Zealand

Trade Me

Enterprise Knowledge Search · IT Operations Automation

Trade Me wanted to turn a marketplace and engineering organization into an AI-first ecosystem, which meant replacing physical data centers, accelerating incident diagnosis, and letting employees query marketplace trends without specialized code.

Achieved 4 9s availability for critical APIs through
to extract complex marketplace trends with natural language and decommissioned 100% of physical data centers to modernize operations on

Reported impact

Business outcome

Trade Me shortened triage cycles, improved API reliability, reclaimed staff time from toil, and created a natural-language interface to marketplace intelligence.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Trade Me uses Google Cloud to address this challenge: Trade Me wanted to turn a marketplace and engineering organization into an AI-first ecosystem, which meant replacing physical data centers, accelerating incident diagnosis, and letting employees query marketplace trends without specialized code. Gemini Enterprise Agent Platform provides the managed agent runtime that powers internal automation and future engineering-efficiency gains, serving as the serving layer. Agent Development Kit wraps existing APIs and enables specialized agents such as the Incident Agent, serving as the orchestration layer. BigQuery grounds the marketplace agents in operational and trend data that employees can query in natural language, serving as the storage layer. Cloud Run supports the critical APIs that now run with four nines of availability, serving as the serving layer. AlloyDB backs the critical API layer in the cloud-native marketplace stack, serving as the storage layer.

Data flow

Marketplace and observability data is centralized in BigQuery, ADK-based agents call existing APIs through Gemini Enterprise Agent Platform, Cloud Run and AlloyDB underpin critical marketplace services, and users across the business query trends or incident context in plain English.

Solution architecture

6 components · 4 layers
  1. Compute
    • Gemini pulls together observability, operational, and marketplace data for analysis and agentic coding workflows
  2. Storage
    • BigQuery grounds the marketplace agents in operational and trend data that employees can query in natural language
    • AlloyDB backs the critical API layer in the cloud-native marketplace stack
  3. Serving
    • Gemini Enterprise Agent Platform provides the managed agent runtime that powers internal automation and future engineering-efficiency gains
    • Cloud Run supports the critical APIs that now run with four nines of availability
  4. Orchestration
    • Agent Development Kit wraps existing APIs and enables specialized agents such as the Incident Agent

Architecture clues

  • Critical APIs improved from three nines to four nines of availability
  • The company decommissioned 100% of its physical data centers
  • The organization is targeting 20% efficiency gains through agentic coding tools

Evidence from the source

Achieved 4 9s availability for critical APIs through
Agents connect directly to BigQuery to extract complex marketplace trends using natural language.
Critical APIs moved from 3 9s to a robust 4 9s of availability.
Slashed incident triage by up to one week with custom Gemini agents built on the Agent Development Kit (ADK).
to extract complex marketplace trends with natural language and decommissioned 100% of physical data centers to modernize operations on
Transparency Evidence & methodology 85/100 reference value · 79% 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 value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 confidence79%
  • 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
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • 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. Google Cloud

    Trade Me customer story

    First-party
    https://cloud.google.com/customers/trademe
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • Sitemap last modified Aug 6, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Aug 6, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.