Huawei Cloud Telecommunications Guangdong, China Company newsroom

China Mobile Guangdong

Sales Automation

China Mobile Guangdong wanted to upgrade enterprise support operations so account managers could handle enterprise services faster, reduce repetitive preparation work, and improve customer experience under its AI+ strategy.

reduced average business processing time by 30%
reducing hours of preparation work to just minutes
supported over 150,000 scenario invocations

Reported impact

Business outcome

The deployment shifted enterprise support from human-machine division of labor to intelligent collaboration, shortened processing and proposal preparation time, upgraded enterprise services, and supported large-scale operational use.

  • Customer Experience
  • Productivity Improvement

Solution

China Mobile Guangdong and Huawei built the "AI+BOSS" program around an "AI Account Manager Assistant" that serves as a dedicated AI partner for each account manager. Huawei's self-developed LLM Engine provides the underlying data and model foundation, explicitly supporting data collection, model training, and service deployment so the assistant can run on a unified, secure platform with second-level interface switching and zero data leakage claims. On top of that platform, Huawei applied its AI-Native architecture to deliver intent recognition, intelligent agent orchestration, and autonomous AI decision-making for telecom-specific workflows. A joint expert team from Huawei and China Mobile Guangdong continuously refined the models and prompts, then pushed changes through agile delivery and continuous integration from prototype validation to commercial deployment. Huawei also added self-learning feedback loops and KPI-based evaluation so the assistant keeps improving after launch while maintaining carrier-grade reliability at scale.

Data flow

Enterprise support information is collected through Huawei's LLM Engine, which provides the shared data and model foundation for training and deploying telecom-specific AI capabilities. Those capabilities are exposed through the AI Account Manager Assistant, where account managers retrieve integrated information and generate personalized proposals, while feedback and KPI evaluation are used to refine the models over time.

Solution architecture

3 components · 3 layers
  1. Compute
    • LLM Engine Provides the unified data and model foundation for data collection, model training, and service deployment.
  2. Serving
    • AI Account Manager Assistant Acts as a dedicated AI partner for each account manager, integrating information and generating personalized proposals.
  3. Orchestration
    • AI-Native architecture Supports intent recognition, intelligent agent orchestration, and autonomous AI decision-making for telecom operations.

Architecture clues

  • A self-learning feedback optimization mechanism and KPI-based evaluation system were added for ongoing improvement.
  • Huawei states the platform enables second-level interface switching.
  • Huawei states the solution ensures zero data leakage.
  • The architecture is described as carrier-grade reliable.
  • The project uses agile delivery and continuous integration from prototyping validation to commercial deployment.
  • Unified platform covers data collection, model training, and service deployment.

Evidence from the source

"Based on its AI-Native architecture concept ... Huawei built a solution featuring intent recognition, intelligent agent orchestration, and autonomous AI decision-making."
"Huawei leveraged its self-developed LLM Engine to provide a unified data and model foundation, supporting an integrated capability that covers data collection, model training, and service deployment."
"In the 'AI+BOSS' project, by building an 'AI Account Manager Assistant', China Mobile Guangdong achieved a transformation from 'human-machine division of labor' to 'intelligent collaboration' in enterprise support."
"Since its launch, the project has successfully supported over 150,000 scenario invocations."
"The 'AI Account Manager Assistant' acts as a dedicated AI partner for every account manager, helping them efficiently integrate information and generate personalized proposals, reducing hours of preparation work to just minutes."
"The initiative has reduced average business processing time by 30%, enabling a comprehensive upgrade in enterprise services."
Transparency Evidence & methodology 81/100 reference value · 94% 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 value81/100
  • 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 specificity16/20
    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 confidence94%
  • 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
    6 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. China Mobile Guangdong

    China Mobile Guangdong customer story

    First-party
    https://www.huawei.com/en/huaweitech/cases/chinamobile-guangdong-huawei-ai-boss
    Source type
    Customer-published story
    Published
    Nov 14, 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 14, 2025 · Huawei · 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.