Normalized claim
Time: 70% decrease
Multilingual model development time reduced by 70%.
AIHelp builds multilingual AI customer service agents using Vertex AI and Gemini
Use case typeCustomer service automationUpdated Jun 13, 2026
AIHelp provides AI-powered customer service to enterprise gaming customers in 32 countries and more than 40 languages. The company adopted Google Cloud Vertex AI as a centralized MLOps platform and Gemini as the core LLM, with Vertex AI RAG/vector search, Translation AI, and Vertex AI Search to improve multilingual response quality and support agents.
- Organization
- AIHelp
- Industry
- Tech & Comms
- Location
- United States
- Published
- May 2026
Reported outcomes
−70%
timeTime & speed
5-10%quantified impact+30%productivity
Strategic outcomes
Customer experience & trustImproved multilingual customer support qualitySpeed & agilityCentralized model training and deployment workflowCustomer experience & trustReduced hallucinations in complex scenariosCustomer experience & trustEnabled faster first-round issue resolution
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Quantified impact: 5-10% increase
User satisfaction increased by 5-10%.
Normalized claim
Productivity: 30% increase
Customer service efficiency improved by 30%.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- AIHelp
- Provider
- GCP
- Maturity
- Exploring
- Linked source
- Google Cloud Customer Stories
Used Translation AI and Vertex AI summarization/paraphrasing/evaluation to translate and quality-check responses
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 2 of 2
- 1Customer Service Automation
- 2Multilingual AI
Limited language comprehension in some languages; hallucinations in complex scenarios; low response accuracy; high human review effort.
- Used Vertex AI to automate model training, testing, and deployment in a centralized MLOps workflow.
- Used Gemini for multi-round conversations, intent reasoning, and context memory.
- Used Vertex AI RAG/vector search to retrieve relevant knowledge base content and reduce hallucinations.
- Used Translation AI and Vertex AI summarization/paraphrasing/evaluation to translate and quality-check responses.
- Used Vertex AI Search to help support agents find answers faster.
Technologies
- Multilingual model development time reduced by 70%.
- User satisfaction increased by 5-10%.
- Customer service efficiency improved by 30%.
- Less human review and more first-round resolution.
Architecture
AIHelp uses Vertex AI as a centralized MLOps platform that automates model training, testing, and deployment. Gemini serves as the core LLM for multi-round conversations and intent reasoning. Vertex AI RAG/vector search retrieves relevant knowledge base content, Translation AI handles multilingual translation, Vertex AI summarization/paraphrasing/evaluation checks response quality, and Vertex AI Search helps support agents find answers faster.
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: May 30, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
Explore related AI use cases
Customer evidence
Provider evidence
Browse the catalog
Was this useful?
Community
Comments
Loading comments...