customer_support · ecommerce · workflow
Rakuten Group builds LLM-powered AI products for business clients and employees using LangChain and LangSmith
Rakuten Group saw an opportunity to augment client and employee support at scale with AI, as large-company teams were developing ideas independently with no systematic way to identify and share effective approaches across the organization.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Client seeks business support
When clients onboard their businesses, they receive support from a dedicated onboarding consultant, and once live, continue to get help from them.
Tools used
LangChainLangSmithOpenGPTs
Outcome
Rakuten deployed a suite of AI products for business clients and an internal employee chatbot platform built by three engineers in one week; the platform is planned to reach 32k employees with an aim to improve productivity by 20%.
Results
Time savedone week
Volumethree engineers
Running sinceJanuary 2023
Grounding & classification
Source type: vendor customer story
30 fields verified against source quotes.
agentic workflowai agentchatbotknowledge searchragsummarizationknowledge basemetric backednamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedecommerceemployee productivitytime savedvendor customer storyback office opscustomer supportit supportsales opsagentic task executionrag answering