Customer Service Chat Intelligence (FIXED)

Scenario Overview

Organization: Global Telecom Service Provider with B2B focus Challenge: Enable intelligent customer service for complex enterprise offerings Scale: 10,000+ enterprise customers, 500+ service products, 200+ locations Integration: ServiceNow CMDB, Salesforce CRM, Product Literature

Technical Workflow

Step 1: Initialize Knowledge Graph System

# Setup customer service environment
mkdir -p customer-service/{literature,cmdb,pricing,analysis}
cd customer-service

# Initialize FalkorDB for service knowledge
netintel-ocr kg init \
  --falkordb-host localhost \
  --falkordb-port 6379 \
  --graph-name customer_service

# Set environment for service catalog processing
export FALKORDB_GRAPH=customer_service
export KG_MODEL=RotatE
export KG_EPOCHS=100

Step 2: Ingest Service Documentation

# Process solution literature with KG generation
netintel-ocr process batch literature/solutions/ \
  --pattern "*.pdf" \
  --kg-model RotatE \
  --extract-tables \
  --output-dir analysis/solutions/

# Process service datasheets in batch
netintel-ocr process batch literature/datasheets/ \
  --pattern "*.pdf" \
  --output-dir analysis/datasheets/

# Process each document with KG
for doc in literature/*.pdf; do
  netintel-ocr kg process \
    --model RotatE \
    --epochs 100 \
    "$doc"
done

# Verify extraction
netintel-ocr kg stats --format table

# View detailed statistics
netintel-ocr kg stats --format json > analysis/stats.json

# Example Output:
# Graph Statistics:
#   Total nodes: 3,421
#   Total edges: 12,456
#   Node types: Service, Feature, Location, Price, Specification
#   Edge types: HAS_FEATURE, AVAILABLE_AT, PRICED_AT, REQUIRES

Step 3: Build Service Knowledge Base

# Import service data using Cypher queries
netintel-ocr kg query \
  "CREATE (s:Service {name:'SDWAN Enterprise', category:'Network'})
   SET s.features = ['Dual-circuit', 'Firewall', 'Zero-Trust']
   RETURN s" \
  --format json

# Import location data
netintel-ocr kg query \
  "LOAD CSV WITH HEADERS FROM 'file:///locations.csv' AS row
   CREATE (l:Location {city:row.city, state:row.state, pop:row.pop})" \
  --format json

# Create service-location relationships
netintel-ocr kg query \
  "MATCH (s:Service), (l:Location)
   WHERE s.name = 'SDWAN Enterprise' AND l.pop = 'Available'
   CREATE (s)-[:AVAILABLE_AT]->(l)" \
  --format json

# Train embeddings on service catalog
netintel-ocr kg train-embeddings \
  --model RotatE \
  --epochs 150 \
  --force

Step 4: Customer Query Processing

# Example customer inquiry using RAG
cat > customer_query.txt << EOF
We need SDWAN for 15 locations in Northeast US with
firewall service and zero trust VPN. What options do we have?
Our requirements:
- 5000 users
- 1Gbps primary, 500Mbps backup
- PCI compliance required
- Budget: $50K-100K monthly
EOF

# Process query with Enhanced MiniRAG
netintel-ocr kg rag-query \
  "$(cat customer_query.txt)" \
  --mode hybrid \
  --context-depth 3 \
  --temperature 0.7 > analysis/query_response.txt

# Find services matching requirements using hybrid search
netintel-ocr kg hybrid-search \
  "SDWAN firewall zero-trust northeast 15-locations" \
  --strategy adaptive \
  --limit 10 \
  --expand-hops 2 > analysis/service_matches.json

# Use Cypher to find specific service combinations
netintel-ocr kg query \
  "MATCH (s:Service)-[:HAS_FEATURE]->(f:Feature)
   WHERE f.name IN ['SDWAN', 'Firewall', 'Zero-Trust']
   MATCH (s)-[:AVAILABLE_AT]->(l:Location)
   WHERE l.region = 'Northeast'
   RETURN s.name, collect(f.name) as features, count(l) as locations
   ORDER BY locations DESC" \
  --format json > analysis/available_services.json

Step 5: Service Comparison and Analysis

# Find similar services using embeddings
netintel-ocr kg find-similar "SDWAN-Enterprise" \
  --limit 5 \
  --threshold 0.7 > analysis/similar_services.json

# Compare specific services
netintel-ocr kg similarity "SDWAN-Enterprise" "SASE-Premium" \
  --method cosine > analysis/service_comparison.txt

# Cluster services to identify patterns
netintel-ocr kg cluster \
  --n-clusters 5 \
  --method kmeans > analysis/service_clusters.json

# Visualize service landscape
netintel-ocr kg visualize \
  --method tsne \
  --dimensions 2 \
  --color-by category \
  --save-plot analysis/service_landscape.png

Step 6: Pricing and Bundle Analysis

# Query pricing information
netintel-ocr kg query \
  "MATCH (s:Service)-[:PRICED_AT]->(p:Price)
   WHERE s.name CONTAINS 'SDWAN'
   RETURN s.name, p.monthly, p.setup, p.discount
   ORDER BY p.monthly" \
  --format json > analysis/pricing.json

# Find bundles using graph traversal
netintel-ocr kg query \
  "MATCH (b:Bundle)-[:INCLUDES]->(s:Service)
   WHERE ALL(req IN ['SDWAN', 'Firewall'] WHERE
     EXISTS((b)-[:INCLUDES]->(:Service {category: req})))
   RETURN b.name, collect(s.name) as services, b.price" \
  --format json > analysis/bundles.json

# Use RAG to analyze pricing options
netintel-ocr kg rag-query \
  "What is the most cost-effective bundle for SDWAN with firewall for 15 locations?" \
  --mode hybrid > analysis/pricing_recommendation.txt

# Find paths between customer requirements and solutions
netintel-ocr kg path-find "Customer-Requirements" "SDWAN-Bundle" \
  --max-depth 4 > analysis/solution_paths.json

Step 7: Location Intelligence

# Query for location availability
netintel-ocr kg query \
  "MATCH (l:Location {state:'NY'})<-[:AVAILABLE_AT]-(s:Service)
   WHERE s.category = 'SDWAN'
   RETURN l.city, l.pop, collect(s.name) as services" \
  --format json > analysis/ny_availability.json

# Find nearest service points
netintel-ocr kg query \
  "MATCH (c:CustomerSite {name:'NYC-Office'})
   MATCH (p:POP)
   WITH c, p, distance(c.coordinates, p.coordinates) as dist
   WHERE dist < 50
   RETURN p.name, p.services, dist
   ORDER BY dist
   LIMIT 5" \
  --format json > analysis/nearest_pops.json

# Analyze coverage gaps
netintel-ocr kg rag-query \
  "Which customer locations lack SDWAN coverage and what alternatives exist?" \
  --mode hybrid \
  --context-depth 2 > analysis/coverage_analysis.txt

Step 8: Chat Bot Integration

# Process customer conversations
cat > chat_conversation.txt << EOF
Customer: What SDWAN options do you have for retail locations?
Customer: We need something that works with our existing Cisco routers
Customer: Can you handle 200 locations across the US?
Customer: What about PCI compliance for our stores?
Customer: How quickly can you deploy?
EOF

# Process each question with RAG
while IFS= read -r question; do
  if [[ ! -z "$question" ]]; then
    echo "Q: $question"
    netintel-ocr kg rag-query "$question" \
      --mode hybrid \
      --temperature 0.7
    echo "---"
  fi
done < chat_conversation.txt > analysis/chat_responses.txt

# Batch process multiple customer queries
cat > batch_queries.txt << EOF
What SDWAN options are available for retail?
Do you support Cisco router integration?
Can you handle 200 locations nationwide?
Are your solutions PCI compliant?
What is the typical deployment timeline?
EOF

netintel-ocr kg batch-query batch_queries.txt \
  --output analysis/batch_responses.json \
  --parallel 4

Step 9: Competitive Analysis

# Compare services using embeddings
netintel-ocr kg similarity "Our-SDWAN" "Competitor-SDWAN" \
  --method cosine > analysis/competitive_similarity.txt

# Find unique features
netintel-ocr kg query \
  "MATCH (s:Service {vendor:'Us'})-[:HAS_FEATURE]->(f:Feature)
   WHERE NOT EXISTS((:Service {vendor:'Competitor'})-[:HAS_FEATURE]->(f))
   RETURN s.name, collect(f.name) as unique_features" \
  --format json > analysis/differentiators.json

# Analyze competitive position
netintel-ocr kg rag-query \
  "How does our SDWAN solution compare to major competitors in terms of features and pricing?" \
  --mode hybrid \
  --context-depth 4 > analysis/competitive_analysis.txt

Step 10: Reporting and Analytics

# Export complete service knowledge graph
netintel-ocr kg export \
  --format json \
  --include-embeddings \
  --output analysis/service_graph.json

# Export for visualization
netintel-ocr kg export \
  --format graphml \
  --output analysis/service_graph.graphml

# Generate service catalog summary
netintel-ocr kg stats --format summary > analysis/catalog_summary.txt

# View embedding statistics
netintel-ocr kg embedding-stats --detailed > analysis/embedding_stats.txt

# Create executive summary using RAG
netintel-ocr kg rag-query \
  "Generate an executive summary of our service catalog capabilities and coverage" \
  --mode hybrid \
  --temperature 0.5 > analysis/executive_summary.txt

Python Integration Example

from netintel_ocr.kg import HybridRetriever, FalkorDBManager
import asyncio

async def handle_customer_query(query_text: str):
    # Initialize components
    manager = FalkorDBManager(
        host="localhost",
        port=6379,
        graph_name="customer_service"
    )

    retriever = HybridRetriever(
        falkor_manager=manager,
        milvus_client=None  # If using Milvus
    )

    # Process query
    results = await retriever.hybrid_search(
        query=query_text,
        strategy="adaptive",
        limit=10
    )

    return results

# Example usage
query = "SDWAN solutions for 15 northeast locations with firewall"
results = asyncio.run(handle_customer_query(query))

Performance Metrics

Actual Achievable Performance

• Query response time: 2-3 seconds (using RAG)
• Cypher query time: 50-200ms
• Embedding similarity search: 100-300ms
• Hybrid search: 1-2 seconds
• Batch processing: 10-20 queries/minute

Accuracy Metrics (Realistic)

• Entity extraction: 85-90%
• Relationship extraction: 80-85%
• RAG response relevance: 75-85%
• Embedding similarity accuracy: 70-80%

Commands Reference (Only Valid Commands)

# Essential customer service commands that actually work
netintel-ocr kg init                           # Initialize KG system
netintel-ocr kg process document.pdf           # Process documents
netintel-ocr kg train-embeddings               # Train embeddings
netintel-ocr kg stats                          # View statistics
netintel-ocr kg query "[Cypher]"              # Query graph
netintel-ocr kg rag-query "[question]"        # Natural language query
netintel-ocr kg hybrid-search "[search]"      # Hybrid search
netintel-ocr kg find-similar "[entity]"       # Find similar items
netintel-ocr kg cluster                       # Cluster entities
netintel-ocr kg path-find "[from]" "[to]"    # Find paths
netintel-ocr kg export --format json          # Export graph
netintel-ocr kg batch-query queries.txt       # Batch processing