Real Impact, Delivered

The Challenge

ComplyAdvantage was drowning in false positives while simultaneously missing sophisticated fraud patterns. Their rule-based system couldn't keep pace with evolving threats, resulting in massive undetected risk exposure.

• 30% of fraudulent transactions going undetected
• 48-hour average detection delay causing cascading losses
• Manual review bottlenecks limiting scale
• No visibility into complex multi-party fraud networks

Our Approach

We architected a comprehensive ML-powered fraud detection system that processes transactions in real-time. By combining advanced anomaly detection with graph neural networks, we created a system that learns and adapts to new fraud patterns automatically.

• Real-time streaming architecture processing 1M+ transactions/hour
• Graph neural networks mapping complex fraud relationships
• Ensemble ML models continuously learning from new patterns
• Automated risk scoring with explainable AI for compliance

The Impact

The new system transformed ComplyAdvantage's fraud detection capabilities overnight. What was once a reactive process became a proactive shield, catching fraud attempts before they could cause damage. The $9.6M in annual risk reduction paid for the entire project in less than 60 days.

The Challenge

SwarmZero's AI agents were producing generic responses that failed to meet user expectations. The lack of contextual understanding was causing high abandonment rates and limiting the monetization potential of their marketplace.

• 68% of users abandoning agents after first interaction
• Generic responses lacking domain expertise
• No ability to reference historical context
• Agents unable to handle complex, multi-step queries

Our Approach

We implemented a sophisticated RAG (Retrieval-Augmented Generation) pipeline that enhances every agent on the platform with contextual knowledge and domain expertise. This allows agents to provide accurate, relevant responses based on vast knowledge bases.

• Vector database for efficient knowledge retrieval
• Dynamic context injection based on user queries
• Multi-modal document processing (PDFs, docs, images)
• Semantic search with relevance scoring

The Impact

The RAG enhancement transformed SwarmZero's marketplace overnight. Agents that previously provided generic responses now deliver expert-level insights. User engagement skyrocketed, and developers saw their agent revenues increase by 5x on average.

The Challenge

This mid-size logistics company was experiencing escalating accident rates and insurance costs. Their reactive approach to safety meant problems were only addressed after costly incidents had already occurred.

• $5.8M annual accident-related costs
• 23% year-over-year insurance premium increases
• No predictive risk assessment capabilities
• Fragmented data across multiple systems

Our Approach

We built a multimodal AI system that ingests telemetry, weather data, and driver behavior patterns to predict and prevent accidents before they occur. The system provides real-time risk alerts and personalized safety coaching.

• Real-time risk scoring for 10M+ miles monthly
• Computer vision for driver fatigue detection
• Weather and route optimization integration
• Automated safety coaching and intervention

The Impact

The predictive safety system transformed their operations from reactive to proactive. Drivers now receive real-time coaching, routes are optimized for safety, and the company has become an industry leader in safety metrics. The $4.2M in annual savings has been reinvested in fleet expansion.