Amazon SageMaker – Trains and continuously retrains time-series ML models using historical and live IoT sensor data for anomaly detection.

Amazon Bedrock & Nova – Powers Retrieval-Augmented Generation (RAG) for Generative AI, contextualizing sensor data with technical manuals, maintenance logs, and facility history.

AWS IoT Core – Collects and processes millions of real-time sensor readings (flow rates, temperature, humidity) from devices across hundreds of facilities.

AWS Lambda – Executes low-latency AI inference and orchestrates real-time responses between models, dashboards, and facility systems.

Amazon DynamoDB – Provides scalable, low-latency storage for structured data such as model metadata, anomaly classifications, and user interactions.

Amazon OpenSearch – Serves as a vector store to retrieve historical anomalies, AI responses, and contextually similar events using semantic search.

Amazon API Gateway – Manages secure API endpoints, enabling smooth communication across the AI stack and user interfaces.

LangChain – Facilitates orchestration between Generative AI models and the retrieval layer, allowing the AI copilot to surface property-specific recommendations.

Vector Databases (OpenSearch with Vector Search support) – Enables semantic retrieval of prior anomaly data, AI responses, and related documentation.

FastAPI – Powers the backend APIs for dashboard access and real-time AI model interaction within the Evolution® platform.

MLOps Pipeline (CI/CD) – Automates model versioning, testing, deployment, and rollback, ensuring high availability and continuous improvement.

Interactive Dashboards & Alerts (SMS, Email, App) – Provide real-time visualizations and notifications to facility managers, enabling immediate response to anomalies.