Service Overview
AI Chatbot
AI chatbot service built on Large Language Models, supporting Japanese OEM trainees and internal teams across WhatsApp, Line, and Facebook Messenger in real time. Multilingual coverage in English, Hindi, and Chinese — backed by an internal knowledge base and internet search. Deployed in 4 months for 1,200+ trainees (scaling to 3,000), achieving 80% accuracy and sub-5-second responses.
80%+
UAT accuracy
<5s
response time
$500/mo
monthly cost
3,000+
trainees supported
4 months
delivery time
Capabilities
Key capabilities
Multi-Platform Support
Deploy across WhatsApp, Line, Facebook Messenger, and custom channels from one core.
Multilingual Support
Real-time support in English, Hindi, and Chinese with natural language understanding.
Knowledge Base + Internet
Answers backed by internal documents with internet search fallback for broader queries.
Human Escalation
Seamless handoff to human agents with full conversation context preserved.
LLM-Powered Chatbot
Built on the latest Large Language Models for natural, context-aware conversations at scale.
Technology
Technology stack
| Component | Technology | Purpose |
|---|---|---|
| LLM | GPT-4, Claude, LLaMA | Natural language understanding |
| Platforms | WhatsApp, Messenger, Telegram | Multi-channel deployment |
| Backend | AWS Lambda, Cloud Functions | Serverless infrastructure |
| Database | DynamoDB, Firestore | Conversation storage |
| RAG | Vector DB, Embedding models | Knowledge retrieval |
Use cases
Real-world applications
Documented outcomes from actual deployments.
Japanese OEM Trainee Multilingual Chatbot
AI chatbot deployed for 1,200+ Japanese OEM trainees in Japan, handling queries across WhatsApp, Line, and Facebook Messenger in English, Hindi, and Chinese — backed by an internal knowledge base and internet fallback.
Before
100% manual staff support, response bottlenecks as trainee base grew to 1,200+
After
AI chatbot handles multilingual queries 24/7 across three major messaging platforms
MBD Engineering Q&A Assistant
ChatAgent integrated into Model-Based Design workflows, providing interactive Q&A, automatic problem diagnosis, and navigation through a centralized engineering knowledge base.
Before
Engineers spending significant time on routine MBD inquiries and troubleshooting
After
AI handles routine queries, rule checks, and knowledge navigation automatically
Scaled Trainee Support Operations
Phased rollout — human-operated chat portal first, then AI integration — enabling sustainable scaling without proportional staff increase.
Before
Manual support model unsustainable at 1,200 trainees with plans to reach 3,000
After
AI-assisted platform sustains growing trainee base at controlled cost
How we work
Implementation approach
Phase 1: Requirements & Integration Planning
- Define chatbot use cases and conversation flows
- Identify integration points with existing systems
- Plan multi-platform deployment strategy
Phase 2: Chatbot Development
- Develop conversation flows and response templates
- Train models on domain-specific knowledge
- Implement human escalation workflow
Phase 3: Integration & Testing
- Integrate with messaging platforms
- Conduct user acceptance testing
- Validate conversation quality and accuracy
Phase 4: Deployment & Monitoring
- Deploy to production environment
- Monitor performance and user satisfaction
- Continuously improve based on conversation analytics
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