UI / UX Design
Instacart's Multilingual Support
Our goal was to improve the support experience for non-English speaking shoppers while reducing inefficiencies in internal support workflows. The challenge was to design a low-cost, scalable language solution that enhances the customer journey and positions the company for future international expansion.
Year :
2025
Industry :
Tech
Client :
Instacart
Project Duration :
4 weeks
Problem :
Non-English speaking shoppers faced significant barriers:
They were often routed to English-speaking agents first, increasing frustration and support handle time.
The process led to higher operational costs, as English agents couldn't assist but still took time to transfer shoppers to appropriate language specialists.
The company faced limitations in global expansion, where legal and service requirements demanded more inclusive language support.
Problem Statement
How might we design a low-cost solution that can grow into future plans of language expansion and improve the experience for both external customers and internal agents?
Approach :
Competitive Audit & Prototype
We conducted a competitive audit to explore how others integrate language switching. Five UI concepts were proposed and evaluated for feasibility, usability, and scalability:
Header 1 | Pros | Cons |
|---|---|---|
#1 - Language Icon Switcher | ✅ Recognizable, compact | ❌ Flags represent countries, not languages; limited screen space |
#2 - Language Dropdown Selector (Text) | ✅ Familiar interface | ❌ Timing of language selection unclear; dropdown too large for chat context |
#3 - Change Language in Settings | ✅ Useful for Spanish (most common alt language) | ❌ Not scalable; would require full app translation. |
#4 - Language Selection in Chat Flow *Selected* | ✅ Easy to implement using existing components ✅ Allows dynamic, on-the-fly switching ✅ Directly connects users to correct language agents | No alarming blockers recognized. |
#5 - Pop-Up Before Chat Start | ✅ Visually prominent, elegant UX | ❌ Engineering limitations due to chatbot being not fully native |
Solution :
PHASE 1 — UI/ Conversational solution
As part of Phase 1, we included design option #4 as a language selection solution embedded the chat flow.
Minimal development effort
Seamless user experience
Leverages current chat architecture
Example interaction—
"Choose preferred language" → Shopper selects preferred language → Direct transfer to appropriate agent
Results Post-Launch
🔻 Reduction in agent-to-agent transfers
🔺 Improved CSAT scores for non-English shoppers
💸 Lowered staffing and training costs
📊 Improved accuracy in language-based workforce forecasting
PHASE 2 — AI Translation Integration
To further streamline support and prepare for global scalability, we began exploring AI-powered live translation within the chat flow. This would enable English-speaking agents to assist any customer, regardless of language.
Tools Explored:
Google Translate API
AWS Translate
Microsoft Azure Translator
Key Considerations:
🧪 Translation accuracy in conversational contexts
💵 API cost vs. business benefit
⚙️ Integration time with existing chat backend
🔐 Data privacy and compliance
________________________________________________________________________________________________________________
Impact & Takeaways :
This two-phase approach created a scalable foundation for multilingual support that:
Significantly improved shopper experience and inclusivity
Created cost-saving opportunities through operational efficiency
Aligned with future business goals for global market expansion
Key Lesson
Solving for language isn't just a UX problem — it's a business enabler.
Starting with simple UI changes can pave the way for sophisticated, AI-driven transformation.
UI / UX Design
Instacart's Multilingual Support
Our goal was to improve the support experience for non-English speaking shoppers while reducing inefficiencies in internal support workflows. The challenge was to design a low-cost, scalable language solution that enhances the customer journey and positions the company for future international expansion.
Year :
2025
Industry :
Tech
Client :
Instacart
Project Duration :
4 weeks
Problem :
Non-English speaking shoppers faced significant barriers:
They were often routed to English-speaking agents first, increasing frustration and support handle time.
The process led to higher operational costs, as English agents couldn't assist but still took time to transfer shoppers to appropriate language specialists.
The company faced limitations in global expansion, where legal and service requirements demanded more inclusive language support.
Problem Statement
How might we design a low-cost solution that can grow into future plans of language expansion and improve the experience for both external customers and internal agents?
Approach :
Competitive Audit & Prototype
We conducted a competitive audit to explore how others integrate language switching. Five UI concepts were proposed and evaluated for feasibility, usability, and scalability:
Header 1 | Pros | Cons |
|---|---|---|
#1 - Language Icon Switcher | ✅ Recognizable, compact | ❌ Flags represent countries, not languages; limited screen space |
#2 - Language Dropdown Selector (Text) | ✅ Familiar interface | ❌ Timing of language selection unclear; dropdown too large for chat context |
#3 - Change Language in Settings | ✅ Useful for Spanish (most common alt language) | ❌ Not scalable; would require full app translation. |
#4 - Language Selection in Chat Flow *Selected* | ✅ Easy to implement using existing components ✅ Allows dynamic, on-the-fly switching ✅ Directly connects users to correct language agents | No alarming blockers recognized. |
#5 - Pop-Up Before Chat Start | ✅ Visually prominent, elegant UX | ❌ Engineering limitations due to chatbot being not fully native |
Solution :
PHASE 1 — UI/ Conversational solution
As part of Phase 1, we included design option #4 as a language selection solution embedded the chat flow.
Minimal development effort
Seamless user experience
Leverages current chat architecture
Example interaction—
"Choose preferred language" → Shopper selects preferred language → Direct transfer to appropriate agent
Results Post-Launch
🔻 Reduction in agent-to-agent transfers
🔺 Improved CSAT scores for non-English shoppers
💸 Lowered staffing and training costs
📊 Improved accuracy in language-based workforce forecasting
PHASE 2 — AI Translation Integration
To further streamline support and prepare for global scalability, we began exploring AI-powered live translation within the chat flow. This would enable English-speaking agents to assist any customer, regardless of language.
Tools Explored:
Google Translate API
AWS Translate
Microsoft Azure Translator
Key Considerations:
🧪 Translation accuracy in conversational contexts
💵 API cost vs. business benefit
⚙️ Integration time with existing chat backend
🔐 Data privacy and compliance
________________________________________________________________________________________________________________
Impact & Takeaways :
This two-phase approach created a scalable foundation for multilingual support that:
Significantly improved shopper experience and inclusivity
Created cost-saving opportunities through operational efficiency
Aligned with future business goals for global market expansion
Key Lesson
Solving for language isn't just a UX problem — it's a business enabler.
Starting with simple UI changes can pave the way for sophisticated, AI-driven transformation.
UI / UX Design
Instacart's Multilingual Support
Our goal was to improve the support experience for non-English speaking shoppers while reducing inefficiencies in internal support workflows. The challenge was to design a low-cost, scalable language solution that enhances the customer journey and positions the company for future international expansion.
Year :
2025
Industry :
Tech
Client :
Instacart
Project Duration :
4 weeks
Problem :
Non-English speaking shoppers faced significant barriers:
They were often routed to English-speaking agents first, increasing frustration and support handle time.
The process led to higher operational costs, as English agents couldn't assist but still took time to transfer shoppers to appropriate language specialists.
The company faced limitations in global expansion, where legal and service requirements demanded more inclusive language support.
Problem Statement
How might we design a low-cost solution that can grow into future plans of language expansion and improve the experience for both external customers and internal agents?
Approach :
Competitive Audit & Prototype
We conducted a competitive audit to explore how others integrate language switching. Five UI concepts were proposed and evaluated for feasibility, usability, and scalability:
Header 1 | Pros | Cons |
|---|---|---|
#1 - Language Icon Switcher | ✅ Recognizable, compact | ❌ Flags represent countries, not languages; limited screen space |
#2 - Language Dropdown Selector (Text) | ✅ Familiar interface | ❌ Timing of language selection unclear; dropdown too large for chat context |
#3 - Change Language in Settings | ✅ Useful for Spanish (most common alt language) | ❌ Not scalable; would require full app translation. |
#4 - Language Selection in Chat Flow *Selected* | ✅ Easy to implement using existing components ✅ Allows dynamic, on-the-fly switching ✅ Directly connects users to correct language agents | No alarming blockers recognized. |
#5 - Pop-Up Before Chat Start | ✅ Visually prominent, elegant UX | ❌ Engineering limitations due to chatbot being not fully native |
Solution :
PHASE 1 — UI/ Conversational solution
As part of Phase 1, we included design option #4 as a language selection solution embedded the chat flow.
Minimal development effort
Seamless user experience
Leverages current chat architecture
Example interaction—
"Choose preferred language" → Shopper selects preferred language → Direct transfer to appropriate agent
Results Post-Launch
🔻 Reduction in agent-to-agent transfers
🔺 Improved CSAT scores for non-English shoppers
💸 Lowered staffing and training costs
📊 Improved accuracy in language-based workforce forecasting
PHASE 2 — AI Translation Integration
To further streamline support and prepare for global scalability, we began exploring AI-powered live translation within the chat flow. This would enable English-speaking agents to assist any customer, regardless of language.
Tools Explored:
Google Translate API
AWS Translate
Microsoft Azure Translator
Key Considerations:
🧪 Translation accuracy in conversational contexts
💵 API cost vs. business benefit
⚙️ Integration time with existing chat backend
🔐 Data privacy and compliance
________________________________________________________________________________________________________________
Impact & Takeaways :
This two-phase approach created a scalable foundation for multilingual support that:
Significantly improved shopper experience and inclusivity
Created cost-saving opportunities through operational efficiency
Aligned with future business goals for global market expansion
Key Lesson
Solving for language isn't just a UX problem — it's a business enabler.
Starting with simple UI changes can pave the way for sophisticated, AI-driven transformation.