Building an Asian-market AI CEO persona for OpenClaw with native Chinese thinking

What this is
A developer has shared their experience building Eve, an AI CEO persona specifically designed for Hong Kong, Taiwan, and mainland China markets, addressing the common problem of English-based personas with poor Chinese translation quality.
The core problem being solved
Most "Asian" AI personas are just English personas with Google Translate quality Chinese that don't understand:
- Cantonese business idioms vs Mandarin formal tone
- Different urgency signals across HK/TW/CN markets
- Cultural calendar (CNY prep starts 6 weeks early, not 1 week)
- Local platforms: HKTVmall, LINE, WhatsApp Business vs WeChat
Key implementation details
Three separate voice modes: HK (Traditional Chinese + Cantonese slang), TW (Traditional Chinese + Taiwan-specific terms), CN (Simplified Chinese + formal tone). Same persona, three different outputs.
Asian-specific memory decay: Built a Hot/Warm/Cold tier where recent customer interactions decay faster than business relationship data. Guanxi (relationships) isn't transactional.
Platform-aware routing: The persona knows which platform it's on (WhatsApp Business, LINE, etc.) and adjusts message structure accordingly.
Local competitor monitoring: Built Algolia-based scrapers that update twice daily for platforms like HKTVmall with their own ecosystems.
Challenges encountered
- Cantonese has almost no good training data, requiring hand-crafting of idiom examples
- CN requires simplified Chinese + formal register, which sometimes conflicts with the persona's casual CEO voice
- Getting autonomous heartbeat working across timezones (HK office hours vs CN factory hours) took 3 iterations
Open questions
- How keigo levels in Japanese map to persona "tone settings"
- How to handle the persona switching languages mid-conversation (common with HK users who code-switch Cantonese/English)
📖 Read the full source: r/openclaw
👀 See Also

OpenClaw user builds character chat app with agentic coding approach
A self-described non-technical OpenClaw user developed a working character chat application in 7 days using agentic coding, noting that their role shifted to reviewing AI-generated work rather than traditional programming.

AI-generated 3D-printable pegboard from hand-drawn sketch
A developer used Codex to convert a hand-drawn sketch into parametric 3D models for a pegboard toy, specifying only two dimensions: 40mm hole spacing and 8mm peg width. The repository contains Python generators for seven play pieces, four gears, and printable boards.

OpenClaw user shares architecture for 43-agent production system
A branding consultancy with 1,000+ clients has been running a 43-agent OpenClaw system in production for months, featuring a layered architecture with specialized agents across command, intelligence, content, tech, and sales functions.

Deploying AI Receptionists for Local Businesses with OpenClaw and Retell AI
A developer deployed AI receptionists using OpenClaw and Retell AI to handle calls for local service businesses, capturing 7 appointments from 23 calls in the first week at a cost of $4.12.