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Google Cloud Telecommunications Canada

TELUS

Customer Service Automation · Enterprise Knowledge Search · Real-time Analytics

TELUS needed to break down channel silos, analyze far more than a tiny fraction of its support interactions, and automate customer-service resolution without exposing sensitive data or forcing customers and agents to restart conversations at every handoff.

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$53.9M in annual operational cost savings
22 million annual interactions
87% faster
automate 30% of customer calls

Reported impact

Business outcome

TELUS turned its contact-center data into an always-on operational AI system, resolving issues much faster, automating a meaningful share of calls, and identifying customer problems with far more certainty than its previous siloed setup allowed.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

TELUS built an agentic customer-experience stack on Google Cloud with BigQuery as its central data foundation. Raw conversational data from 22 million annual calls flows into BigQuery, which serves as an Agentic Data Cloud across more than 14 petabytes of structured and unstructured TELUS data. Sensitive Data Protection scrubs private information before Gemini platform analyzes the full interaction set for sentiment, root causes, and operational patterns. Those insights feed Generative Playbooks and Quality AI coaching so Gemini for customer experience can respond with better guidance, preserve memory as customers move between channels, and assist frontline employees in real time. The result is a unified system that connects zero-copy data in BigQuery with customer-facing and agent-facing Gemini services, turning previously fragmented contact-center analytics into operational AI that can continuously improve every interaction.

Data flow

Customer calls are ingested into BigQuery, where Sensitive Data Protection removes private details. Gemini platform analyzes the interactions for sentiment and root causes, generates coaching and playbooks, and then Gemini for customer experience uses that context to automate responses or support human agents across channels.

Solution architecture

4 components · 4 layers
  1. Compute
    • Gemini platform Analyzes all customer interactions for sentiment, root causes, and Quality AI coaching signals, then powers Generative Playbooks.
  2. Storage
    • BigQuery Acts as TELUS's Agentic Data Cloud, storing and exposing conversational and enterprise data for downstream AI workloads.
  3. Serving
    • Gemini for customer experience Uses the analyzed context to automate customer responses, preserve memory across channels, and guide frontline agents in real time.
  4. Governance
    • Sensitive Data Protection Scrubs private details from customer interactions before the data is analyzed by Gemini-based workflows.

Architecture clues

  • BigQuery serves as the central Agentic Data Cloud
  • Generative Playbooks are built from Gemini platform insights
  • Quality AI analyzes acoustic nuances such as long silences and customer tone of voice
  • Sensitive Data Protection automatically scrubs private details

Evidence from the source

A key capability of the Gemini platform, Quality AI analyzes acoustic nuances.
Gemini for customer experience maintains a continuous 'memory' as customers move between channels.
Raw conversational data from 22 million annual calls is ingested into BigQuery.
Sensitive Data Protection automatically scrubs private details, allowing Gemini platform to safely analyze every call for sentiment and root causes.
TELUS uses BigQuery, Gemini for customer experience, and Gemini platform to automate 30% of customer calls and resolve them 87% faster.
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
    3 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

    TELUS customer story

    First-party
    https://cloud.google.com/customers/telus-gemini-enterprise-cx
    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

  • First archived Jun 27, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    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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