Service Overview
AI Chatbot
FPT Automotive's conversational AI layer built on GPT-4, Claude, and LLaMA with Retrieval-Augmented Generation. Supporting multilingual trainee Q&A on WhatsApp/Line/Messenger, MBD engineering problem-diagnosis, factory-floor database queries (Factory GPT), and warranty defect classification. Replaces manual workloads with secure, natural-language interfaces, achieving 80% UAT accuracy, 60% search time reductions, and multi-stage pipeline automation.
80%+
Trainee UAT accuracy
60%
Factory search reduction
2 stages
Warranty workflow automated
<5s
Trainee response time
Multi-Agent
Enterprise orchestration
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, handling multilingual queries across WhatsApp, Line, and Facebook Messenger in English, Hindi, and Chinese. Phased rollout from human-operated portal to AI-assisted platform — scaling sustainably from 1,200 to 3,000+ trainees at fixed cost.
Before
100% manual staff support with growing bottlenecks; model unsustainable as trainee base scaled toward 3,000
After
AI chatbot handles multilingual queries 24/7 across three major platforms, scaling to 3,000+ trainees
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
Factory GPT — 60% Faster Information Search
On-premise conversational GPT for factory floors, integrated with business databases and data lakes. Managers query production data — machine downtime, product quantities, worker schedules — in natural language and receive instant answers or charts.
Before
Data spread across multiple systems; finding simple facts required jumping between tools and teams
After
Single conversational interface with instant answers and optional graph-based visualizations
Automotive Warranty Repair Classification AI
IvyChat-powered generative AI system automating the end-to-end warranty classification pipeline — ingesting repair case data, classifying against historical defect records, surfacing similar past cases, and proposing remediation actions.
Before
Expert-dependent manual process to classify invoice-level defect data as past or new issues
After
Single AI-driven workflow replaces multi-step manual pipeline; engineers query via natural language
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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