Representative Work

Challenge

High-volume support team spending significant time on repetitive inquiries, leading to long wait times and high costs.

Approach

Deployed an AI-powered support agent using RAG architecture with company knowledge base. Implemented evaluation harness tracking deflection rate, resolution quality, and escalation patterns.

Solution

Production Pilot delivered in 5 weeks with AI Control Tower monitoring quality, cost, and business metrics. System integrated with existing ticketing system and CRM.

Metrics Tracked

• Deflection rate: 52% of inquiries resolved without human agent
• Average handle time: 38% reduction for deflected tickets
• Cost per resolved ticket: 45% reduction
• Customer satisfaction: Maintained at baseline levels

Key Features

• RAG system with semantic search over knowledge base
• Real-time quality monitoring and escalation detection
• A/B testing framework for prompt optimization
• Monthly executive readouts with business impact tracking

Challenge

Manual document processing causing delays, errors, and high operational costs. Needed to extract structured data from unstructured documents with high accuracy.

Approach

Combined LLM-based extraction with validation rules and human-in-the-loop for edge cases. Implemented evaluation suite tracking extraction accuracy, processing time, and error rates.

Solution

Production Launch completed in 10 weeks with full integration into existing workflow systems. AI Control Tower tracks extraction quality, processing latency, and business metrics.

Metrics Tracked

• Processing cycle time: 42% reduction
• Extraction accuracy: 94% (up from 78% manual baseline)
• Error rate: 68% reduction in manual corrections needed
• Throughput: 2.8x increase in documents processed per day

Key Features

• Multi-model routing for optimal cost and accuracy
• Validation rules and confidence scoring
• Human-in-the-loop workflow for low-confidence extractions
• Cost optimization through intelligent model selection

Challenge

Generic recommendation system not driving conversion lift. Needed personalized recommendations based on user behavior and product attributes.

Approach

Deployed fine-tuned recommendation model with real-time personalization. Implemented A/B testing framework to measure conversion lift and revenue impact.

Solution

Production Launch in 8 weeks with integration into product pages and checkout flow. AI Control Tower tracks conversion metrics, revenue impact, and user engagement.

Metrics Tracked

• Conversion rate: 18% lift in recommended product clicks
• Revenue per user: 12% increase for users seeing recommendations
• User engagement: 28% increase in time on product pages
• Recommendation relevance: 89% user satisfaction score

Key Features

• Real-time personalization based on user behavior
• A/B testing framework for continuous optimization
• Revenue impact tracking and attribution
• Model performance monitoring and retraining pipeline