AI Customer Service Skill

AI Customer Service & Recommendations

AI Customer Service

Capabilities

• Natural language query understanding
• Context-aware conversations
• Multi-turn dialogues
• Knowledge base integration
• Escalation to human agents

Use Cases

• Answer platform FAQs
• Guide new users through onboarding
• Help with billing/quotas
• Explain verification processes
• Troubleshoot common issues

Conversation Management

• Session state tracking in Redis
• Conversation history storage in MongoDB
• Context window management
• Fallback to human support

Integration Points

• Platform documentation
• Billing system
• Verification workflows
• Account management
• Company/job search

Intelligent Recommendations

Recommendation Types

Job/Company Recommendations

• Based on user profile and preferences
• Industry, location, role matching
• Historical interaction analysis
• Explainable reasoning

Investor-Founder Matching

• Investment criteria alignment
• Stage and industry fit
• Geographic preferences
• Past deal history

Content Discovery

• Posts relevant to user interests
• Companies in focus areas
• Projects matching skills
• Products for potential collaboration

Recommendation Engine

// Generate recommendations
func RecommendJobs(userID string) []Job {
    profile := GetUserProfile(userID)
    preferences := GetUserPreferences(userID)
    history := GetUserHistory(userID)

    candidates := MatchJobs(profile, preferences)
    scored := ScoreCandidates(candidates, history)
    ranked := RankByRelevance(scored)

    return ranked[:10]
}

NLP Processing

Query Understanding

• Intent classification
• Entity extraction
• Query expansion
• Disambiguation

Response Generation

• Template-based responses
• Contextual information insertion
• Dynamic content rendering
• Multi-language support

Language Support

• English (primary)
• Chinese (Simplified)
• Extensible to other languages

Data Models

AI System

• Conversation - Chat session records
• Message - Individual messages (user + AI)
• Recommendation - Generated recommendations
• Feedback - User feedback on AI responses

User Tracking

• UserPreferences - Preference data
• InteractionHistory - Clicks, views, actions
• RecommendationImpressions - Shown recommendations

Common Tasks

Implement AI Chat

1. Create chat component in frontend
2. WebSocket or polling for real-time
3. Backend endpoint for message processing
4. NLP intent classification
5. Response generation logic
6. Store conversation history

Build Recommendation System

1. Define recommendation criteria
2. Implement matching algorithm
3. Score and rank candidates
4. Add explainability (why this match?)
5. A/B test recommendation quality
6. Track user engagement

Integrate Knowledge Base

1. Structure platform documentation
2. Index for semantic search
3. Map queries to answers
4. Update answers as platform evolves
5. Track query patterns for gaps

Handle Escalation

1. Detect complex/ambiguous queries
2. Route to human support
3. Share conversation context
4. Track resolution time
5. Learn from escalations