Huawei Cloud Commercial and Professional Services China Translated from Chinese

Thinla

Marketing Personalization

Thinla needed to scale a global influencer-marketing platform, keep API performance stable under large international campaign loads, and improve the precision of its creator-matching model without losing cost control.

Read the original story Explore all use cases

100,000 API calls per second

99.99% uptime

campaign ROI improved by 40%

influencer-selection precision improved by 25%

Reported impact

Business outcome

Thinla improved influencer-tagging precision, raised client campaign ROI, and kept its global marketing platform stable while entering new overseas markets.

Solution

Thinla deployed its influencer-marketing platform and Tide influencer AI model on Huawei Cloud to scale global campaigns while making creator matching more precise. CCE hosts the platform's microservices and absorbs up to 100,000 API calls per second, while WAF and traffic monitoring protect the public interfaces and trigger elastic scaling. ECS and LTS feed a real-time monitoring layer so campaign metrics and logs can be analyzed continuously and strategy changes can be made during live programs. Compute resources are centrally scheduled behind ELB to keep utilization and costs under control. On the AI layer, Ascend compute optimizes Thinla's self-developed Tide model for influencer retrieval, attribute recognition, and brand matching. Data from campaigns and creator interactions flows through the platform into model scoring and back to marketers as higher-precision creator recommendations.

Data flow

Campaign events, creator attributes, API traffic, and monitoring logs flow through the CCE-hosted platform into ECS/LTS monitoring and ELB-managed compute. Ascend-backed model scoring then identifies the best influencer matches and returns optimized recommendations and campaign insights to marketers.

Solution architecture

6 components · 4 layers

Compute
- Ascend compute Provides the AI compute layer used to optimize Thinla's influencer-matching model.
Serving
- CCE Hosts the influencer-marketing platform microservices and supports high-concurrency API traffic.
- ECS and LTS real-time monitoring platform Collect and analyze campaign metrics and logs so teams can adjust live marketing strategies.
- Tide influencer marketing AI model Scores influencer retrieval, attribute recognition, and brand matching for global marketing campaigns.
Orchestration
- ELB Coordinates and balances compute resources to improve utilization and cost efficiency.
Governance
- WAF Protects public APIs and supports stable operation under campaign load.

Architecture clues

Ascend compute is used specifically to optimize Thinla's AI matching model.
CCE runs the core high-concurrency platform services.
ECS and LTS provide real-time campaign monitoring.
WAF and traffic monitoring are part of the elastic-scaling path.

Evidence from the source

"Ascend AI compute helped Thinla optimize influencer retrieval, attribute recognition, and brand matching for its self-developed model. (original: Thinla与华为云昇腾算力合作，利用其强大的AI能力优化潮汐红人营销AI模型，高效应对海量红人需求的检索、属性识别和品牌匹配等需求。)"

"Thinla deployed its compute on Ascend Cloud and used that AI capability to optimize the Tide influencer-marketing AI model, improving creator-selection precision by 25% and customer ROI by 40%. (original: 星莱云创基于业务需求将算力部署到昇腾云，利用昇腾强大的AI能力优化潮汐红人营销AI模型，使得红人筛选的精准度提升了25%，Thinla助力客户营销活动的ROI提升了40%。)"

"Thinla deployed the influencer-marketing platform on Huawei Cloud CCE so it could withstand 100,000 API calls per second and maintain 99.99% uptime. (original: 通过部署华为云的云容器引擎（CCE），Thinla的红人营销平台成功应对了高达10万次每秒的API调用压力... 即使在市场推广的高峰期，系统依然保持99.99%的稳定运行时间。)"

"Through fine-grained resource scheduling, Thinla improved server utilization by 20%, cut operating cost by 15%, and saved more than 20,000 US dollars per month. (original: Thinla利用华为云的负载均衡（ELB）和弹性计算资源，通过精细化的资源调度管理，将服务器资源的利用率提升了20%。同时，通过自动化的资源调度策略，Thinla成功将运营成本降低了15%... 通过对计算资源的高效管理和调度，Thinla实现了每月节约超过2万美元的运营成本。)"

Transparency Evidence & methodology 100/100 reference value · 83% confidence · 2 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

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 value100/100

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 impact25/25
7 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence83%

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
4 direct source quotes retained.
Date provenance0/10
The publication date has no documented provenance.
Source transparency10/10
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.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

Huawei Cloud
Thinla联合华为云打造全球红人市场营销解决方案-客户案例-华为云

First-party
https://www.huaweicloud.com/cases/xinglaiyunchuang.html

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
Guangdong University of Foreign Studies Career Service
深圳星莱云创科技有限责任公司 - 广东外语外贸大学就业信息网

Independent secondary
https://career.gdufs.edu.cn/web/index/company-info?id=69268
The employer profile lists company scale as 50-99人, which falls within the SMB band.

Source type
Company-submitted public employer profile
Supports
- Company size

Date evidence

First archived Dec 14, 2024 · 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. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Similar use cases

See all like this

Databricks Jul 8, 2026 MiQ Commercial and Professional Services Global MiQ needed a better way to support agent-assisted media planning and campaign optimization because massive audience and campaign datasets were spread across separate tools, forcing planners to manually stitch together… Number of campaigns for more than 2,300 advertisers powered by Sigma (since launch) Enterprise Knowledge SearchMarketing Personalization AI Search · Agent Bricks · Databricks Data + AI Platform

Google Cloud Modified · Jun 19, 2026 Genscom Commercial and Professional Services Belgium Genscom wanted to automate highly personalized print campaigns at scale, but it needed to digitize legacy print assets, combine live customer data with physical production, and handle order spikes without sacrificing… Marketing PersonalizationSales Automation BigQuery · Cloud Run functions · Firestore

Google Cloud Archived · May 16, 2026 NextAfter Commercial and Professional Services United States With clients ranging from higher education institutions to advocacy groups, NextAfter strives to provide high-level, multi-channel insights. The challenge? Doing so with a two-person data team that supports a broader… 1,000 hours saved by consolidating dashboards into a single view with Looker IT Operations AutomationMarketing Personalization BigQuery · Gemini · Gemini Enterprise