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Google Cloud Retail Australia

New Aim

Real-time Analytics · Supply Chain Optimization

New Aim needed to evolve from a large retailer into an infrastructure platform that could optimize supply chain, product performance, and multi-channel commerce with AI agents, while keeping latency and incident response under control.

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120,000 sq m fulfillment footprint
Processing transactions across more than 40 distinct sales channels
billions of tokens weekly
crashing infrastructure incident response times from hours to just 15 minutes

Reported impact

Business outcome

New Aim improved uptime and response time while turning retail and logistics data into a continuously accessible AI operating layer that helps managers make faster decisions across channels and inventory.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

New Aim built an AI-native operating layer called AimCore on Google Cloud to manage transactions and logistics across more than forty sales channels and a 120,000-square-meter fulfillment footprint. Cloud Storage underpins a hybrid data lake for transaction and logistics data, while BigQuery serves as the core warehouse and uses native ML plus Gemini Flash models to analyze customer behavior, SKU performance, and internal telemetry at machine speed. The company also runs 24/7 internal agents through Gemini Enterprise Agent Platform, processing billions of tokens each week so planners can ask operational questions such as days of stock in plain English. Applications and transaction endpoints are deployed through Cloud Run to reduce latency for offshore operations. This stack lifted uptime, slashed incident response time, and turned what had been manual retail analysis into a continuous, AI-assisted commerce and supply-chain workflow.

Data flow

Transaction and logistics data is centralized in Cloud Storage and BigQuery, where BigQuery ML and Gemini Flash agents analyze supply-chain and product-performance questions. Results feed planners and operational applications deployed on Cloud Run for ongoing commerce decisions.

Solution architecture

6 components · 4 layers
  1. Compute
    • BigQuery ML Runs granular predictive models across products and commerce activity.
    • Gemini Flash Powers internal operational agents that answer planning questions and automate SKU analysis.
  2. Storage
    • Cloud Storage Holds the hybrid data lake containing transaction and logistics data across channels and fulfillment operations.
    • BigQuery Acts as the core warehouse for behavior, telemetry, and operational analysis across AimCore.
  3. Serving
    • Cloud Run Deploys applications and transaction endpoints with low-latency global access.
  4. Orchestration
    • Gemini Enterprise Agent Platform Hosts the 24/7 internal agents that process large token volumes for operational decision-making.

Architecture clues

  • AimCore is New Aim's proprietary AI-native operating layer
  • BigQuery ML is used to eliminate manual product and operational analysis friction
  • Cloud Run hosts global applications and endpoints
  • Gemini Flash agents are used for 24/7 internal operations

Evidence from the source

Leveraging native capabilities like BigQuery, ML, and Gemini Flash
Processing transactions across more than 40 distinct sales channels
To successfully transition from a high-scale retailer to an ecosystem infrastructure provider, New Aim built a proprietary, AI-native operating layer known as AimCore.
deploy 24/7 internal operational agents
lifting service uptime from 97% to 99% and crashing infrastructure incident response times from hours to just 15 minutes.
Transparency Evidence & methodology 95/100 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 value95/100
  • 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 impact25/25
    2 metrics 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

    New Aim customer story

    First-party
    https://cloud.google.com/customers/newaim
    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 Jun 16, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Jun 16, 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.

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