Building an intelligent customer service system with OpenClaw requires more than just the AI agent itself — you need a complete toolchain for knowledge management, conversation analytics, quality assurance, and operational efficiency. This collection covers the essential tools that transform a basic chatbot into a professional customer service operation.
# Batch upload product documentation
for file in /docs/products/*.pdf; do
curl -X POST http://localhost:3000/api/knowledge/upload \
-F "file=@$file" \
-F "category=products" \
-H "Authorization: Bearer $API_KEY"
echo "Uploaded: $file"
done
| Tool | Purpose | Integration |
|---|---|---|
| CSV Importer | Bulk FAQ upload | OpenClaw API |
| Version Control | Track FAQ changes | Git-based |
| Auto-Suggestion | Identify FAQ gaps from logs | Built-in analytics |
| Multi-Language | Manage translations | i18n system |
#!/bin/bash
# check_knowledge_health.sh
echo "=== Knowledge Base Health ==="
echo "Total documents: $(curl -s http://localhost:3000/api/knowledge/stats | jq '.total_docs')"
echo "Last updated: $(curl -s http://localhost:3000/api/knowledge/stats | jq '.last_update')"
echo "Orphaned docs: $(curl -s http://localhost:3000/api/knowledge/stats | jq '.orphaned')"
echo "Coverage score: $(curl -s http://localhost:3000/api/knowledge/stats | jq '.coverage_pct')%"
Deploy on Tencent Cloud Lighthouse — simple, high-performance, cost-effective — and set up conversation analytics:
# analytics_collector.py
from collections import Counter
from datetime import datetime
class ConversationAnalytics:
def __init__(self):
self.metrics = {
'total_conversations': 0,
'resolved_by_ai': 0,
'escalated_to_human': 0,
'avg_messages_per_conversation': [],
'topic_distribution': Counter(),
'hourly_volume': Counter(),
}
def track_conversation(self, conversation):
self.metrics['total_conversations'] += 1
if conversation['resolved_by'] == 'ai':
self.metrics['resolved_by_ai'] += 1
else:
self.metrics['escalated_to_human'] += 1
self.metrics['avg_messages_per_conversation'].append(
conversation['message_count']
)
self.metrics['topic_distribution'][conversation['topic']] += 1
hour = datetime.fromisoformat(conversation['started_at']).hour
self.metrics['hourly_volume'][hour] += 1
def get_resolution_rate(self):
total = self.metrics['total_conversations']
if total == 0:
return 0
return self.metrics['resolved_by_ai'] / total * 100
# unresolved_tracker.py
def track_unresolved_questions(conversations):
"""Identify questions the AI couldn't answer"""
unresolved = []
for conv in conversations:
if conv['escalated'] or conv['satisfaction'] < 3:
unresolved.append({
'question': conv['first_message'],
'topic': conv['detected_topic'],
'frequency': 1,
'timestamp': conv['started_at']
})
# Aggregate by similarity
grouped = group_similar_questions(unresolved)
return sorted(grouped, key=lambda x: x['frequency'], reverse=True)
# qa_checker.py
class ResponseQualityChecker:
def check(self, response, context):
issues = []
# Length check
if len(response) > 1000:
issues.append("Response too long (>1000 chars)")
if len(response) < 20:
issues.append("Response too short (<20 chars)")
# Tone check
negative_words = ['unfortunately', 'cannot', 'impossible', 'never']
negative_count = sum(1 for w in negative_words if w in response.lower())
if negative_count > 2:
issues.append("Excessive negative language")
# Accuracy check - verify claims against knowledge base
claims = self.extract_claims(response)
for claim in claims:
if not self.verify_against_kb(claim):
issues.append(f"Unverified claim: {claim[:50]}...")
# Sensitive data check
if self.contains_pii(response):
issues.append("Response contains potential PII")
return {
'quality_score': max(0, 100 - len(issues) * 20),
'issues': issues,
'passed': len(issues) == 0
}
# ab_testing.yml
experiments:
- name: "greeting_style"
variants:
A: "Hi! How can I help you today?"
B: "Welcome! What can I assist you with?"
metric: "customer_satisfaction"
sample_size: 500
- name: "response_length"
variants:
A: { max_tokens: 300 }
B: { max_tokens: 500 }
metric: "resolution_rate"
sample_size: 1000
# escalation_tools.py
class EscalationManager:
def __init__(self):
self.rules = {
'auto_escalate': [
'account_security',
'billing_dispute_over_100',
'legal_request',
'angry_customer_3_messages'
],
'escalation_targets': {
'billing': 'billing-team@company.com',
'technical': 'tech-support@company.com',
'general': 'support-queue@company.com'
}
}
async def escalate(self, conversation, reason):
target = self.determine_target(conversation['topic'])
# Create ticket with full context
ticket = {
'conversation_history': conversation['messages'],
'customer_info': conversation['customer'],
'ai_summary': await self.generate_summary(conversation),
'escalation_reason': reason,
'priority': self.calculate_priority(conversation)
}
await self.create_ticket(target, ticket)
await self.notify_customer(
"I'm connecting you with a specialist who can better assist you."
)
{
"responses": {
"greeting": "Hi there! I'm your AI assistant. How can I help you today?",
"escalation": "Let me connect you with a specialist who can help with this.",
"closing": "Is there anything else I can help you with?",
"after_hours": "Our human agents are currently offline. I can help with most questions, or I can create a ticket for follow-up.",
"feedback_request": "Was this helpful? Your feedback helps me improve!"
}
}
# crm_connector.py
async def enrich_customer_context(customer_id):
"""Pull customer data from CRM before AI responds"""
crm_data = await crm_client.get_customer(customer_id)
return {
'name': crm_data['name'],
'plan': crm_data['subscription_plan'],
'tenure_months': crm_data['months_active'],
'open_tickets': crm_data['open_ticket_count'],
'lifetime_value': crm_data['ltv'],
'last_interaction': crm_data['last_contact_date']
}
A professional customer service operation needs knowledge management, analytics, QA, operational tools, and integrations working together. OpenClaw provides the AI engine; these tools provide the operational framework.
Deploy your complete customer service stack on Tencent Cloud Lighthouse — simple, high-performance, cost-effective.
For OpenClaw setup, see the configuration guide.