Baidu AI Cloud Telecommunications China Translated from Chinese

China Telecom

Customer Service Automation

China Telecom needed a differentiated anti-harassment service that could protect subscribers from nuisance calls, preserve privacy and service quality expectations, and scale across a nationwide telecom operation without relying on manual call handling.

Harassment-call interception rate increased to 98%.
The service supports 1.5 million users.
The service supports 8 provinces.

Reported impact

Business outcome

China Telecom expanded an AI-powered call-protection service across multiple provinces, improved harassment-call interception accuracy, and gave subscribers a less disruptive way to manage unwanted calls and missed-call follow-up.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

China Telecom worked with Baidu AI Cloud to build an AI anti-harassment assistant on top of Baidu AI Cloud Intelligent Customer Service. The service intercepts suspicious promotional or nuisance calls, answers them on the user's behalf, identifies the caller's purpose through speech recognition and intent analysis, and compiles a summary for later delivery to the subscriber. The same platform also upgrades missed-call reminders by handling unanswered calls, determining caller intent, and sending structured follow-up information back to the user. Architecturally, the customer-service stack provides the serving layer, speech recognition converts live audio into text, and intent analysis classifies call purpose so the system can decide what to report. Delivery is integrated with official-account and SMS channels, giving customers a less disruptive way to decide whether to call back or ignore the request.

Data flow

Incoming suspicious calls are routed into the AI assistant, where speech recognition transcribes the conversation and intent analysis determines why the caller is calling. The platform then generates a summary and delivers it to subscribers through official-account or SMS notifications so they can choose whether to call back or ignore the request.

Solution architecture

3 components · 3 layers
  1. Ingest
    • Speech Recognition Transcribes intercepted calls so the service can understand what nuisance callers are saying.
  2. Compute
    • Intent Analysis Classifies the purpose of incoming calls and helps determine the summary returned to the user.
  3. Serving
    • Baidu AI Cloud Intelligent Customer Service Runs the anti-harassment call-handling workflows and delivers the user-facing assistant service.

Architecture clues

  • Missed-call reminders were upgraded so the AI assistant can answer unanswered calls and capture caller intent
  • The design balances service differentiation with user-experience and privacy requirements raised by the telecom operator
  • The service answers suspected nuisance calls on behalf of users and returns summaries asynchronously

Evidence from the source

China Telecom and Baidu AI Cloud built an AI anti-harassment intelligent customer service assistant that uses speech recognition and intent analysis to answer and intercept nuisance calls automatically (original: 中国电信联合百度智能云打造AI防骚扰智能客服,通过语音识别与意图分析技术实现骚扰电话自动应答拦截).
The missed-call reminder service was also upgraded so the assistant can answer when the user's phone cannot connect, determine caller intent, and send the result to the user (original: 对『未接通来电提醒』进行服务升级,当用户电话无法接通时,智能客服助手自动接听,智能应答,了解来电方的意图,并发送给用户).
The service covers 8 provinces and 1.5 million users, and the harassment interception rate rose to 98% (original: 覆盖8省150万用户,骚扰拦截率提升至98%).
The summaries are regularly delivered to users through official-account or SMS messages so they can decide whether to call back or ignore the contact (original: 定期通过公众号/短信的方式将汇总信息发送给用户知晓,进而进行回拨或者忽略).
When a number is identified as a promotional or nuisance caller, the assistant answers on the user's behalf, understands the sales content, and summarizes it for later delivery (original: 当判断来电号码为推销/骚扰电话时,代替用户接听电话,了解推销内容概要并汇总).
Transparency Evidence & methodology 90/100 reference value · 89% 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 value90/100
  • 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 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 confidence89%
  • 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 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 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. Baidu AI Cloud

    China Telecom customer story

    First-party
    https://cloud.baidu.com/customer/case/ctc.html
    Source type
    Vendor-published customer story
    Published
    Jul 4, 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 Jul 4, 2025 · Baidu AI Cloud · 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.

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