AWS Lambda
8 use cases using this technology
Aizon Improves Pharmaceutical Manufacturing With Artificial Intelligence Using AWS
Aizon
Aizon built a GxP-compliant AI platform on AWS to help pharmaceutical manufacturers optimize biomanufacturing processes. Using AWS IoT, AWS Lambda, and Amazon SageMaker, Aizon created digital twins of bioreactors that trigger machine learning operations for process optimization. For a multinational pharmaceutical company producing blood-plasma-based drugs, Aizon built unsupervised and supervised learning models that identified two critical process variables; optimizing them improved yield by double digits, worth potentially tens of millions of dollars. AWS also reduced Aizon's compute costs by up to 80 percent.
Epilot reduces email processing time by 87% using Amazon Bedrock
Epilot
Cologne, Germany-based Epilot, which provides an extended-relationship-management (XRM) platform for energy companies, built an AI email-summarization feature on Amazon Bedrock (Anthropic's Claude via serverless AWS Lambda/SQS architecture) that summarizes long customer email threads for its 170+ energy-sector customers. Epilot generates 55,000 AI email summaries monthly with a negligible failure rate; about 80% of users say the feature simplifies their work, and users save 87% of the time previously spent on email management. Epilot also built a 'suggested actions' feature that auto-updates customer records from email content with a human in the loop, and keeps all processed data within an EU AWS Region using Amazon Bedrock's zero-retention data policy.
CarbonTrail cuts generative AI costs by 88% for sustainable emissions intelligence using Amazon Bedrock
CarbonTrail
New Zealand-based CarbonTrail built an AI-powered emissions measurement platform and CarbonAPI on AWS, running a document-analysis pipeline on Amazon Bedrock and workloads on AWS Inferentia to process bank-scale invoice and transactional data. The platform achieves an 87% reduction in processing time and 88% lower cost than a comparable GPT-4-with-embeddings approach, and reduces low-confidence classifications by up to 40%. The Bank of New Zealand (BNZ) uses CarbonTrail's CarbonAPI to progress toward measuring emissions across its SME customers.
Siemens Electronics Factory Erlangen Reduces Machine Learning Deployment Time by 80% with AWS and Siemens Industrial AI on Industrial Edge
Siemens Electronics Factory Erlangen
Siemens Electronics Factory Erlangen, which manufactures PCBs and controllers such as SINAMICS converters and SINUMERIK CNC controllers, used computer vision models to spot anomalies in PCB assembly, but training and retraining ML models on premises was time-intensive and constrained by GPU bottlenecks. The factory adopted AWS services together with Siemens Industrial Edge and Siemens Industrial AI, sending shopfloor images via Edge applications to Amazon S3 before training via Amazon SageMaker or AWS Lambda; training results and edge prediction results are monitored through AI Model Monitor, with AI Model Manager providing central management of models on the shopfloor. This reduced time spent on model training and retraining by 80 percent (from about 30 minutes to roughly 5 minutes for retraining and deployment), cut costs by more than 90 percent compared to on-premises data storage systems, and reduced the false call rate by over 50 percent, while continuously preventing around 4 percent of PCB assembly errors compared to around 60 percent at peaks in the past.
Mercedes-Benz Consulting builds AI-powered digital assistant for natural-language document search
Mercedes-Benz
Mercedes-Benz Consulting transformed how employees access information by building an AI-powered digital assistant that processes natural language queries. Using Amazon SageMaker and AWS Lambda, the platform significantly improved knowledge access across millions of documents.
Novo Nordisk Scales to 2,500+ Use Cases with Secure Generative AI Using Amazon Bedrock
Novo Nordisk
Novo Nordisk built a self-service generative AI platform on AWS, using Amazon Bedrock's foundation models, so employees could build and customize chatbots for nonregulated business use cases without needing to develop applications or maintain infrastructure themselves. The company worked with AWS Partner Cloud2 Oy (previously KeyCore) to validate the architecture for security and scalability. More than 25,000 Novo Nordisk employees have used the platform to create chatbots for over 2,500 unique use cases, such as retrieving information, drafting documents, or acting as a virtual colleague or critic. The company's general-purpose chatbot is used by more than 1,000 employees and processes over 26,000 prompts a month; its largest use case was trained on 140,000 documents. Each use case costs around $10 per month to run on AWS, using serverless services including Amazon DynamoDB and AWS Lambda. Building a chatbot now takes days rather than months, and some tasks that took a full day can be completed in as little as 10 minutes.
NewDay lifts generative AI agent-assist accuracy from 60% to over 90%
NewDay
NewDay, whose contact centre handles 2.5 million calls a year, built NewAssist, a generative AI assistant on Amazon Bedrock using Retrieval Augmented Generation, out of an internal hackathon. Through iterative experiments — including a custom parser for its knowledge base and injecting internal acronyms into prompts — NewDay raised NewAssist's accuracy from below 60% to over 90%, cut the average time to retrieve an answer from 90 seconds to 4 seconds, and rolled it out to over 150 agents, running on serverless AWS infrastructure for under $400 a month.
How Mapfre Insurance modernized fraud claims with Amazon EMR Serverless
Mapfre Insurance
Mapfre Insurance, the number one auto and home insurer in Massachusetts, modernized fraud detection by combining graph-based features from Neo4j with machine learning models deployed on AWS, using Amazon EMR Serverless, Apache Iceberg tables on Amazon S3, AWS Glue Data Catalog, AWS Lake Formation, and Amazon MWAA for orchestration. Fraud predictions integrate directly with Guidewire Claims via AWS Lambda, automatically creating claim activities showing the top model drivers for adjusters. The initiative, covering Massachusetts Auto insurance and later expanded to Home insurance, has delivered more than $5 million in Net Present Value, with detection accuracy improved 50-135 percent compared to baseline methods.