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Google Cloud Software / SaaS Taiwan

17LIVE

Document Processing · Marketing Personalization · Real-time Analytics

"All our content is on live, so to truly optimize the viewing experiences, we need to know what's happening and react fast. However, the data we analyzed didn't include what happened in our streaming rooms," explains Eric Hsu, SVP of Engineering at 17LIVE.

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17LIVE enhances its live streaming experiences by using Google AI to efficiently troubleshoot and create more compelling content.
Enhances troubleshooting efficiency to improve user satisfaction rate by 30% with Gemini
SVP of Engineering, 17LIVE

Reported impact

Business outcome

Enhances troubleshooting efficiency to improve user satisfaction rate by 30% with Gemini; Increases interaction between live streamers and users through AI cohosts powered by Gemini; Identifies users' and livestreamers' personas with Gemini and Vertex AI; to provide more personalized content recommendations.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

Gemini also analyzes logs and historical data stored in BigQuery to detect technical incidents before they escalate. When an issue is identified, a Gemini-powered AI agent automatically initiates the necessary actions and assigns tasks to the appropriate team members. This improved coordination allows 17LIVE's engineers to prevent problems proactively, requiring 30% fewer site reliability engineering resources. Identifies users' and livestreamers' personas with Gemini and Vertex AI to provide more personalized content recommendations For example, to enhance interaction between live streamers and viewers, 17LIVE has built AI cohosts with Gemini to greet and chat with viewers individually on behalf of live streamers. During streaming sessions, AI cohosts can suggest or create topics based on viewers' profiles to enhance engagement. When inappropriate content appears, AI cohosts also act as moderators to take necessary actions.

Data flow

stored or unified in BigQuery, processed with Vertex AI, and surfaced through Gemini, Veo

Solution architecture

4 components · 3 layers
  1. Compute
    • Vertex AI Hosts and orchestrates machine learning and generative AI capabilities inside the solution.
  2. Storage
    • BigQuery Stores and queries the analytics and AI-ready datasets that power the workload.
  3. Serving
    • Gemini Powers natural-language reasoning, summarization, generation, or assistance inside the workflow.
    • Veo Generates video-centric creative content for user experiences.

Architecture clues

  • # 17LIVE: Offering more interactive, personalized live streaming content with Gemini
  • Enhances troubleshooting efficiency to improve user satisfaction rate by 30% with Gemini
  • Generates lively expressions for virtual streamers with Gemini and Veo to engage audience
  • Identifies users' and livestreamers' personas with Gemini and Vertex AI
  • Identifies users' and livestreamers' personas with Gemini and Vertex AI to provide more personalized content recommendations
  • Increases interaction between live streamers and users through AI cohosts powered by Gemini

Evidence from the source

Enhances troubleshooting efficiency to improve user satisfaction rate by 30% with Gemini
Generates lively expressions for virtual streamers with Gemini and Veo to engage audience
Identifies users' and livestreamers' personas with Gemini and Vertex AI
Increases interaction between live streamers and users through AI cohosts powered by Gemini
to provide more personalized content recommendations
Transparency Evidence & methodology 85/100 reference value · 82% confidence · 2 sources

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 confidence82%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 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 transparency9/10
    2 source references 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

    17LIVE customer story

    First-party
    https://cloud.google.com/customers/17live-ai
    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
  2. 17LIVE Group Limited / Singapore Exchange

    17LIVE Group Limited Annual Report 2025

    First-party
    https://links.sgx.com/1.0.0/corporate-announcements/TJ4LMQVHNAMDHD0J/881531_17LIVE-Annual%20Report%20FY2025.pdf

    The report lists the registered office at #14-07 Keppel Bay Tower, Singapore 098632 and FY2025 employee counts of 298 male and 284 female employees.

    Source type
    Official annual report
    Supports
    • Company size

Date evidence

  • First archived Feb 15, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Feb 4, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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