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Google Cloud Telecommunications Canada

Bell Canada

Customer Service Automation

Bell needed to move beyond rigid support decision trees and fragmented agent tooling so it could resolve more customer inquiries autonomously while still personalizing interactions across chat, voice, and human-assisted channels.

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Handled 16M+ chat interactions, with over half resolved by AI
managed over 5 million voice interactions
over 81% of respondents agreed their experience was easy
over 95% understanding of customer intent

Reported impact

Business outcome

Bell handled large support volumes with more autonomous resolution, better intent understanding, and improved employee support, giving customers faster and easier service across multiple channels.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Bell rebuilt its customer-support experience on Google Cloud by combining BigQuery with Gemini for customer experience and employee-facing Gemini tools. BigQuery provides the data foundation that lets Bell activate contextual insights across support touchpoints, while Gemini for customer experience powers virtual assistants that can understand nuanced customer requests instead of following static scripts. These agents resolve complex inquiries in chat and voice, preserve context across channels, and feed better information to employees through Agent Assist so human agents can focus on higher-value work rather than swivel-chairing between disconnected systems. Bell is also using Gemini platform to improve internal workforce proficiency on the same foundation. This architecture turned Bell's support operation into a more autonomous, data-informed service environment that scales to millions of interactions without sacrificing personalization.

Data flow

Customer interaction data is centralized in BigQuery, then used by Gemini for customer experience to drive virtual assistants across chat and voice. When escalation is needed, Agent Assist passes the relevant context to human agents, and Gemini platform supports broader employee workflows on the same foundation.

Solution architecture

4 components · 3 layers
  1. Compute
    • Gemini platform Extends AI capabilities into Bell's internal workforce and broader agentic support strategy.
  2. Storage
    • BigQuery Provides the data foundation that fuels contextual and personalized support experiences.
  3. Serving
    • Gemini for customer experience Runs the autonomous virtual assistants that resolve customer chat and voice interactions.
    • Agent Assist Provides employees with context and recommendations when a customer is transferred to a human agent.

Architecture clues

  • Agent Assist reduces swivel-chairing for human agents
  • BigQuery is used to build the contextual data foundation
  • Gemini for customer experience powers virtual assistants across channels
  • Gemini platform is also used to improve workforce proficiency

Evidence from the source

Bell partnered with Google Cloud to deploy virtual assistants built with Gemini for customer experience.
Bell uses BigQuery and advanced data analytics to build a robust data foundation.
By powering our next-gen virtual assistants with Gemini for customer experience, we’ve seamlessly handled over 16 million chats—resolving more than half autonomously—and managed over 5 million voice interactions.
The virtual assistants have achieved over 95% understanding of customer intent.
over 81% of respondents agreed their experience was easy.
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

    Bell Canada customer story

    First-party
    https://cloud.google.com/customers/bell-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 Aug 29, 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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