Amazon EC2

3 use cases using this technology

3published use cases

2industries

3countries on record

Generative AI

top AI capability

Evidence mix: High 3 · Medium 0 · Low 0 — bands are computed from each record's evidence signals.

Industry

Country

3 use cases

Financial ServicesGenerative AIComputer VisionDocument Intelligence

Empowering Employees to Work Strategically Using Amazon Bedrock with BDM

Big Data Mining (BDM), a Brazilian company, built LOUIS, a bespoke generative AI model on Amazon Bedrock combining computer vision and natural language processing to process complex, unstructured corporate documents (contracts, powers of attorney) across more than 100 models and 15 industries. A large Brazilian financial institution deployed LOUIS to replace a team of 150 professionals manually evaluating over 40,000 unstructured processes per month for opening legal-entity accounts and contracting credit, standardizing interpretation criteria and reducing operational effort by more than 40%, an estimated $4.2 million in cost savings over five years. In insurance, LOUIS reduced life insurance claim processing from up to six months to just a few minutes by capturing required data in about 60 seconds. BDM reports documents are processed 85% faster than manual interpretation with 98% accuracy, and saved roughly 50% on development costs by building on Amazon Bedrock rather than writing the application from scratch.

Big Data Mining (BDM)· BrazilAmazon Bedrock · Amazon S3 · Amazon EC2 +1

Financial ServicesGenerative AIPredictive Analytics

Discover Financial Services Builds a Generative AI Solution on AWS for Faster Decision-Making and Time to Market

Discover Financial Services, a digital banking and payment services company, built an analytics workbench and unified data science workbench on Amazon EC2 (P3 and P4 GPU instances) so its data scientists could run machine learning and generative AI workloads, train large language models with sample sizes requiring up to 6 TB of memory, and deliver high-performance computing in the cloud. Model artifacts are stored in Amazon S3 and shared across engineering teams via Amazon EFS. Discover used the platform for use cases including sentiment analysis of customer service calls and a 'do not contact' model that classifies customers in near real time for customer care agents. Using feature embedding, the team reduced time to market from hours to minutes; parallel model training cut processing of 30 million records from days to hours, and sentiment analysis on a 57,000-record dataset dropped from hours to minutes. Discover reports 35% reduction in engineering and platform costs.

Discover Financial Services· United StatesAmazon EC2 · Amazon S3 · Amazon EFS

Life SciencesGenerative AIPredictive AnalyticsDigital Twins & Simulation

Phagos uses generative AI on AWS to match bacteriophages to bacterial infections

Phagos, a Paris-based biotech startup, uses generative AI models built with Amazon SageMaker AI to match bacteriophages to target bacteria for phage therapy, replacing a manual trial-and-error process. The AI models cut wet-lab testing needs by 50% and reduced phage-candidate screening time by 99.5%, from 29 hours to 10 minutes per bacteria. Phagos has treated more than half a million animals in France and can now develop a new treatment in two months versus 10+ years for traditional antibiotic development.

Phagos· FranceAmazon SageMaker AI · Amazon EC2 · Amazon S3 +1