Unstructured Maintenance Knowledge
Decades of maintenance procedures, equipment manuals, and tribal knowledge locked in PDFs and experienced engineers' heads — inaccessible when technicians need it most during an unplanned outage.
Generative AI on AWS
Generative AI for Manufacturing on AWS
We help manufacturers apply Amazon Bedrock and SageMaker to real factory-floor problems: predictive maintenance advisories, quality control automation, and operational knowledge capture — without disrupting production.
- A use case is scoped against real data.
- The model runs on Amazon Bedrock.
- A knowledge path retrieves from your sources.
- Guardrails apply policy and filters before output leaves.
- A named owner reviews anything that reaches customers or records.
- The feature ships inside the existing application.
Key Challenges We Solve
Quality Inspection at Scale
Manual visual inspection of defects is slow, inconsistent across shifts, and cannot scale with production volume. AI-powered inspection requires training data pipelines and model deployment on the factory floor.
Anomaly Detection Complexity
Configuring threshold-based alerts for thousands of machine sensors produces alert fatigue. ML-based anomaly detection requires data science expertise most manufacturers do not have in-house.
OT/IT Data Silos
Machine data in historians, MES systems, and ERP applications remains siloed, preventing the unified data foundation required for effective AI models.
Our Approach
Bedrock-Powered Maintenance Assistant
RAG-based Q&A system on Amazon Bedrock that ingests equipment manuals, maintenance logs, and SOPs — giving technicians instant, accurate answers about equipment configuration and repair procedures.
SiteWise Native Anomaly Detection
Deploy AWS IoT SiteWise native anomaly detection (no custom ML pipeline required) to identify abnormal equipment behavior across vibration, temperature, and pressure signals — alerting before failures occur.
Unified Manufacturing Data Lake
Consolidate OT historian data, IoT telemetry, MES records, and quality data into a governed S3 data lake using AWS Glue and Lake Formation — the foundation for every AI use case.
Frequently Asked Questions
Which AWS service should we use for manufacturing AI — Bedrock or SageMaker?
Use Amazon Bedrock for generative AI use cases (maintenance Q&A, documentation assistants, report generation) — it requires no ML expertise and provides access to leading foundation models. Use SageMaker for custom predictive models (e.g., equipment failure prediction trained on your specific sensor data). Most manufacturers benefit from both.
Does AWS have anomaly detection built into IoT SiteWise?
Yes. AWS IoT SiteWise native anomaly detection (launched July 2025) detects abnormal patterns in asset properties without requiring a custom ML pipeline. It uses unsupervised learning trained on your historical sensor data and integrates directly with SiteWise asset models.
How do we get machine data into AWS for AI training?
AWS IoT Greengrass v2 gateways on factory-floor servers collect data from PLCs, SCADA systems, and industrial sensors via OPC-UA, Modbus, and MQTT. Data flows to AWS IoT Core and IoT SiteWise for structured storage, then to S3 for AI/ML training pipelines.
What is the typical starting point for manufacturing AI on AWS?
Most manufacturers start with one of two high-ROI use cases: (1) a Bedrock-powered maintenance knowledge assistant that indexes existing manuals and SOPs, or (2) SiteWise anomaly detection on their highest-value equipment. Both deliver results in 6-10 weeks.
How does generative AI integrate with our existing MES or ERP?
Bedrock AgentCore agents can call external APIs, enabling AI assistants to query your MES for work order data, production schedules, and quality records in real time. This allows the AI to provide context-aware answers grounded in live operational data.
Ready to Get Started?
Talk to our AWS experts about generative ai for manufacturing on aws.