Google Cloud Telecommunications Australia Company newsroom

Telstra

Customer Service Automation

Telstra wanted to let children and parents call Santa from its nationwide payphone network during the Christmas season, but a human-only hotline could not answer large volumes of wish-list and question calls in real time while consistently staying child-appropriate and privacy-aware.

Reported impact

Business outcome

Telstra turned its Santa hotline into a real-time generative AI voice service that can answer callers dynamically across its payphone network while enforcing child-safety, privacy, and conversation-isolation controls.

  • Customer Experience
  • Risk Reduction
  • Scalability

Solution

Telstra built a seasonal voice experience that turns public payphones into an AI-powered hotline to Santa. Inbound calls from Telstra payphones are handed into Customer Engagement Suite from Google Cloud, which fronts the interaction and lets callers speak naturally instead of navigating a fixed script. Google’s Gemini large language model generates Santa’s answers in real time using a custom persona, prompt-engineered guardrails, and child-appropriate Christmas-only response rules. Gemini’s custom voice capabilities synthesize the spoken reply from voice recordings made for the project so callers hear a branded Santa voice rather than a generic TTS output. Calls are transcribed in real time for monitoring, transcripts are deleted after 72 hours, and each conversation is isolated so the model stays “frozen” and is not trained on caller data.

Data flow

Children and parents dial in through Telstra payphones, and the call is handled by Customer Engagement Suite from Google Cloud. The conversation is transcribed in real time, Gemini uses prompt-engineered rules and a custom Santa persona to generate a response, and Gemini voice capabilities speak the answer back to the caller. Transcript data is monitored for quality control, retained for up to 72 hours, then deleted.

Solution architecture

3 components · 3 layers
  1. Ingest
    • Telstra payphones They provide the public voice entry point for callers dialing the Santa hotline.
  2. Compute
    • Gemini It generates real-time answers, applies the Santa persona and guardrails, and produces Santa’s synthetic voice for the call.
  3. Serving
    • Customer Engagement Suite from Google Cloud It handles the AI-powered call experience used to answer callers on the hotline.

Architecture clues

  • Calls are transcribed in real time for quality control.
  • Each caller conversation is isolated so the LLM is not trained on hotline conversations and remains 'frozen'.
  • Prompt engineering is used to keep responses child-appropriate and focused only on Christmas topics.
  • Santa’s voice was created from recordings made specifically for the project using Gemini custom voice capabilities.
  • Transcripts are deleted after 72 hours.

Evidence from the source

"Santa will use Google’s Gemini large language model (LLM) and Customer Engagement Suite from Google Cloud to answer everyone’s calls."
"Santa’s voice is generated using Gemini’s custom voice capabilities, which were created from recordings we made with Santa especially for this project."
"While the calls themselves will be transcribed in real-time to make sure that the AI program is working correctly, they will be deleted after 72 hours to help avoid any privacy concerns."
"With the tech from the Google Cloud team, Santa is able to respond dynamically and in real-time – something that wasn’t possible before generative AI."
"the conversations Santa has with each caller are isolated; this means Santa’s LLM isn’t being ‘taught’ based on any of the calls made to the North Pole. It’s ‘frozen’."
Transparency Evidence & methodology 66/100 reference value · 92% 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 value66/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 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 specificity8/10
    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. Telstra

    Telstra customer story

    First-party
    https://www.telstra.com.au/exchange/how-we-re-helping-santa-with-generative-ai
    Source type
    Customer-published story
    Published
    Nov 13, 2025
    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 Nov 13, 2025 · Telstra · 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.