Drunk Elephant

Application Refactoring · Marketing Personalization

In 2019, Shiseido acquired Drunk Elephant , one of the fastest growing skincare brands in the last decade. Drunk Elephant is a highly effective skincare brand with research proven results, established in the United States in 2013. Founded by Tiffany Masterson, Drunk Elephant is renowned for its ingredient-elimination philosophy.

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Reported impact

Business outcome

In addition to the Drunk GPT interactive chatbot, Drunk Elephant’s partnership with Alibaba Cloud has resulted in improved online marketing and security. With the help of Alibaba Cloud, Drunk Elephant runs interactive campaigns on the Drunk Elephant WeChat mini program, including attracting new customers through interactive AI picture generation challenges and running services for consumers via trained industrial models for brands. An example of the latter is allowing a space for AI to provide professional and interactive feedback to customers. These functionalities require multiple interactions with the app.

Solution

Drunk Elephant used Alibaba Cloud to address In 2019, Shiseido acquired Drunk Elephant , one of the fastest growing skincare brands in the last decade. Drunk Elephant is a highly effective skincare brand with research proven results, established in the United States in 2013. Founded by Tiffany Masterson, Drunk Elephant is renowned for its ingredient-elimination philosophy. In 2019, Shiseido acquired Drunk Elephant , one of the fastest growing skincare brands in the last decade. Drunk Elephant is a highly effective skincare brand with research proven results, established in the United States in 2013. Founded by Tiffany Masterson, Drunk Elephant is renowned for its ingredient-elimination philosophy. Drunk Elephant partnered with Alibaba Cloud because it needed to work with a company that had cutting-edge technology, professionalism, and good leverage in China.

Data flow

Drunk Elephant's workloads and source data are processed on Model Studio, Container Service for Kubernetes（ACK）.

Solution architecture

3 components · 3 layers

Compute
- Model Studio Drunk GPT is deployed on an Alibaba Cloud Container Service for Kubernetes (ACK) cluster, its content response is based on the invocation of Qwen models with Alibaba Cloud Model
Orchestration
- Container Service for Kubernetes（ACK） Supports Drunk Elephant's deployment by a Kubernetes-based service that ensures high efficiency for enterprises by running containerized applications on the cloud
Uncategorized
- Tongyi Qianwen (Qwen) Drunk GPT is deployed on an Alibaba Cloud Container Service for Kubernetes (ACK) cluster, its content response is based on the invocation of Qwen models with Alibaba Cloud Model

Architecture clues

Finetuning was performed on top of the Qwen LLM to incorporate industry-specific knowledge and to deliver higher accuracies when responding to consumers’ queries
Primary customer region: Japan

Evidence from the source

Alibaba Cloud’s Tongyi Qwen LLM, which is used by Drunk GPT, has surpassed the world’s leading AI technologies in assessments for Chinese language tasks and achieved the highest score for open source models in MMLU evaluation for English language tasks.

Drunk GPT is deployed on an Alibaba Cloud Container Service for Kubernetes (ACK) cluster, its content response is based on the invocation of Qwen models with Alibaba Cloud Model Studio , and it uses NLS to provide text-to-speech conversion.

Drunk GPT operates via two branches: Directly on Drunk Elephant’s WeChat mini program and through the GPT chat on the Drunk Elephant webpage.

This solution based on retrieval augmented generation (RAG) and multi-agent workflows can effectively integrate Drunk Elephant’s private knowledge, such as product information, and trigger agentic workflows based on the user’s interactions, such as requesting the user to participate in a questionnaire.

To answer Drunk Elephant’s needs, Shiseido worked with Alibaba Cloud to build “Drunk GPT,” an interactive multi-round dialogue LLM that understands text, audio, and images and focuses on skincare knowledge, relevant Q&A, product consultation, and product recommendations.

Transparency Evidence & methodology 66/100 reference value · 77% confidence · 1 source

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 value66/100

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 impact0/25
0 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence77%

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 provenance0/10
The publication date has no documented provenance.
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.

Traceability

Sources & provenance

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

Alibaba Cloud
Drunk Elephant customer story

First-party
https://www.alibabacloud.com/customers/shiseido_drunk_elephant

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

Date evidence

First archived Oct 24, 2025 · 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, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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