Pharmaceutical Commerce & Credit Intelligence Platform

A leading company in the pharmaceutical distribution and trade finance ecosystem wanted to transform its platform into an AI-first intelligence engine capable of improving commerce, payments, lending, and operational decision-making. The existing platform processed large volumes of transactional, distributor, retailer, invoice, and payment data, but critical business decisions still relied heavily on manual workflows, fragmented systems, and historical reporting.

• Fragmented Business Intelligence: Data distributed across ERPs, invoices, payment systems, messaging platforms, and operational databases—no unified view of customers, distributors, and transactions.
• Manual Commerce Operations: Product matching, pricing decisions, scheme interpretation, demand forecasting, and inventory optimization required extensive manual intervention.
• Credit & Lending Risk: Invoice financing decisions depended on limited historical information, making fraud detection and credit risk assessment challenging.
• Limited Real-Time Visibility: Business teams lacked proactive insights into distributor health, retailer risk, payment behavior, market pricing, inventory movement, and sales channel leakage.
• Scaling AI Across Multiple Business Functions: Need for reusable intelligence platform, not isolated AI models.

We designed a cloud-native AI platform on AWS that acts as a centralized intelligence layer for all business operations. The platform combines machine learning, generative AI, document intelligence, semantic search, graph analytics, and event-driven processing into a unified architecture.

• Implemented intelligent product matching using semantic search and vector embeddings, improving medicine catalog accuracy despite inconsistent naming conventions.
• Developed AI services for deal qualification, dynamic pricing recommendations, margin optimization, demand forecasting, inventory replenishment, near-expiry inventory matching, and product substitution recommendations.

• Built machine learning models to predict invoice payment behavior, score credit risk, detect invoice fraud, and identify suspicious transaction patterns.
• Implemented graph analytics to discover collusion and fraud rings, reducing lending risk while accelerating financing decisions.

• Automated document processing for invoices, customer onboarding documents, compliance records, and financial documentation.
• Platform extracts structured information, validates business rules, and cross-verifies data against existing enterprise records.

• Built natural language interface enabling business users to query enterprise data using plain English without writing SQL.
• Executives and operations teams retrieve insights on distributor performance, retailer payment behavior, credit exposure, product movement, and inventory trends in real-time.

• Amazon Bedrock: Foundation models, conversational AI, RAG, and generative AI workflows.
• Amazon SageMaker: ML model training, deployment, forecasting, fraud detection, and risk scoring.
• Amazon OpenSearch Service: Vector search and semantic product matching.
• AWS Lambda & EventBridge: Event-driven AI pipelines and orchestration.
• Amazon Textract & Comprehend: Intelligent document extraction and classification.
• Amazon DynamoDB & RDS: Low-latency operational AI and transactional data.

• Automated qualification of significant commerce opportunities, reducing manual effort and accelerating deal processing.
• Improved product matching accuracy through AI-driven semantic search for reliable catalog management.
• Enhanced payment prediction and credit risk assessment with greater cash flow visibility.
• Faster fraud detection through AI-based invoice analysis and graph-driven anomaly identification.
• Streamlined document processing by automating extraction and validation of business-critical information.
• Real-time operational insights through natural language analytics, enabling faster data-driven decision-making.
• Scalable intelligence platform where every business interaction strengthens future recommendations through continuous learning.