Conversational AI Engineering · 2024–2025 · Honda Motor Co.
Cloning a domain expert's personality into a trusted, bilingual AI safety companion.
This is an internal Honda Motor Co. product. Product name and expert identity are confidential. This case study describes the design process, technical architecture, and interaction decisions — happy to discuss in detail during a conversation.
10+ months
Conversational AI Designer & Engineer
Overview
A bilingual (JP/EN) conversational AI system that models the personality of a real domain expert to deliver trusted cycling-safety guidance in their authentic voice. The core challenge: Japan's cycling regulations are dense and inaccessible — the existing resources are dry and intimidating. The solution makes compliance feel like talking to a knowledgeable friend, not reading a rulebook.
The Problem
Japan's bicycle safety regulations are scattered across dense legal documents. Most cyclists don't know the rules. Existing educational resources are formal and off-putting. The design challenge: how do you make regulatory content engaging and trustworthy enough that people actually absorb it?
Research & Discovery
Multimodal personality analysis — video transcripts, speaking patterns, humor markers, linguistic fillers
Linguistic extraction — sentence endings, discourse markers, characteristic JP/EN code-switching patterns
Regulatory document structuring — chunking cycling rulebook for accurate, citable RAG retrieval
Conversational UX — balancing authentic personality with factual accuracy and citation requirements
Key Insight
“Personality isn't just tone — it's trust. Users engaged more deeply and retained information better when the AI felt like a real person with opinions, warmth, and humor rather than a neutral information retrieval system.”
Design Process
Personality modeling — extracting 54+ behavioral and linguistic traits via multimodal Gemini analysis
Prompt engineering — encoding fillers, humor style, sentence length, warmth markers into system prompts
Initial RAG — FAISS vector search for regulatory retrieval (returned correct but shallow answers)
Migration to Graph+Vector RAG (Neo4j) — connected regulations surface together in one coherent response
Voice avatar integration — MuseTalk for animated face lip-synced to Qwen3-TTS voice cloning output
Frontend — React 19 component library within Honda design system constraints
Critical Pivot
FAISS-only retrieval returned isolated regulation fragments — when a rule connected to three others, users got one. Migrating to Neo4j Graph+Vector RAG let the system traverse the knowledge graph and respond with full regulatory context in a single natural response. This was the turning point from 'accurate but choppy' to 'genuinely feels like an expert.'
Results
Authentic personality validated through user testing — participants reported feeling they were talking to a real expert
Bilingual responses with natural Japanese discourse markers and appropriate cultural humor
Source-cited answers grounding every factual claim in specific regulation articles
Full voice avatar prototype with lip-synced animation received GM stakeholder approval
Reflection
“The hardest design challenge wasn't the RAG pipeline or the voice cloning — it was crafting a system where every filler word, every joke, every moment of hesitation was a deliberate design decision that built trust. The engineering was in service of humanity.”