GNN-Based Anti-Money Laundering (AML)
Problem: Legacy rule-based AML systems suffer from excessive false-positive rates (often >95%), resulting in massive operational overhead and regulatory friction for Tier-1 banks.
AI Solution: We consult on the implementation of Graph Neural Networks (GNNs) to identify non-linear relationships and hidden circular transactions across millions of nodes. By mapping entity relationships in a vector space, we reduce false positives by 40% while identifying sophisticated “smurfing” patterns that traditional systems overlook.
Graph Neural NetworksVector EmbeddingsCompliance
Generative Molecular Design
Problem: The traditional drug discovery lifecycle exceeds 10 years and costs over $2B per successful molecule, largely due to the trial-and-error nature of identifying viable lead compounds.
AI Solution: This consultation focuses on Diffusion Models and Geometric Deep Learning for de novo protein design. By leveraging latent diffusion to generate molecular structures that optimize for specific binding affinities, we help life sciences firms compress the lead-optimization phase from years to months, drastically improving R&D throughput.
Diffusion ModelsProtein FoldingR&D Acceleration
Industrial Digital Twins & IoT
Problem: Unplanned downtime in high-precision manufacturing environments (e.g., semiconductor fabrication) can cost upwards of $1M per hour due to cascading sensor failures and lack of predictive visibility.
AI Solution: We architect Transformer-based time-series forecasting models integrated with NVIDIA Omniverse-powered digital twins. By correlating multi-modal sensor data with historical failure modes, we enable prescriptive maintenance where the AI doesn’t just predict failure, but simulates optimal intervention strategies to maintain throughput.
Digital TwinsTime-Series TransformersIoT
Semantic Search & Discovery
Problem: Keyword-based search engines in large-scale e-commerce fail to capture user intent, leading to high bounce rates when users search for abstract concepts (e.g., “durable outdoor gear for rainy alpine trekking”).
AI Solution: This consultation designs a transition to Vector Search (utilizing Pinecone or Weaviate) combined with Retrieval-Augmented Generation (RAG). By embedding the entire product catalogue into a high-dimensional vector space, we enable semantic matching that understands intent, increasing conversion rates by up to 35% through superior relevance.
Vector DBRAGSemantic Search
Automated Underwriting & Claims
Problem: Insurance carriers handle millions of unstructured documents (medical records, police reports, adjustor notes), leading to multi-week claim processing times and significant leakage through human error.
AI Solution: We provide a roadmap for deploying Multi-modal Large Language Models (LLMs) configured for Chain-of-Thought reasoning. These systems ingest unstructured data, verify them against policy terms in real-time, and generate preliminary adjudication summaries, reducing “time-to-decision” from 14 days to under 120 seconds.
Multi-modal AIDocument IntelligenceUnderwriting
Autonomous Supply Chain Orchestration
Problem: Global supply chains are susceptible to “Bullwhip Effects”—where small fluctuations in demand cause massive, costly swings in inventory—due to fragmented data across siloed ERP systems.
AI Solution: We consult on the development of Agentic AI systems utilizing Reinforcement Learning (RL) for dynamic inventory rebalancing. These agents act autonomously across the supply chain, adjusting procurement orders and routing in response to real-time geopolitical or weather events, ensuring 99.9% SKU availability with minimal capital tied in stock.
Agentic AIReinforcement LearningSCM