SAS vs Siemens

SAS Asset Performance Analytics predicts asset problems and identifies their root causes using analytics and data mining.

SAS Energy Forecasting Cloud generates electric-load and renewable-generation forecasts using statistical and AI models.

SAS Field Quality Analytics identifies emerging product quality issues using predictive analytics and anomaly detection.

SAS Financial Crimes Analytics uses AI and machine learning to detect and investigate financial-crime risks.

Real-time fraud detection and decisioning for financial institutions on SAS Viya: profiles customer behaviour and scores every transaction with machine-learning models, rules and anomaly detection against payment, account-takeover, application, synthetic-identity, check and digital fraud, with alert triage and investigation.

SAS Fraud Decisioning for Claims uses analytical and AI techniques to score insurance claims for fraud.

SAS Grid Guardian AI applies predictive analytics and machine learning to anticipate utility-equipment failures.

SAS Intelligent Planning uses analytics and AI to support demand forecasting, inventory optimization and planning scenarios.

SAS Intelligent Planning Cloud creates demand plans using models that self-tune with machine learning.

SAS Production Quality Analytics uses machine learning to analyze manufacturing processes and predict quality problems.

SAS Text Miner extracts information from text documents and supports text parsing and exploration.

SAS Visual Analytics supports data exploration, analytics and reporting using machine learning and natural language explanations.

SAS Visual Investigator provides investigation management and intelligence analysis using AI-assisted analytics and entity resolution.

SAS Visual Statistics supports exploring data and building predictive models using machine learning.

Natural language processing workspace in SAS Viya that finds topics, extracts key terms and entities, scores sentiment and builds categories across large text collections by combining linguistic rules with machine and deep learning, and curates text to ground LLMs.

Generative AI assistant inside SAS Viya that writes, explains and comments SAS and Python code, suggests model-pipeline nodes in Model Studio, runs Model Manager tasks by conversation, and drafts reports and explanations of results in plain language.

SAS Credit Origination uses AI and machine learning to automate credit decisions.

SAS Customer Intelligence 360 uses AI to turn customer insights into personalized actions across channels.

Decision-intelligence platform on SAS Viya that combines predictive and machine-learning models with business rules and guardrails in governed, versioned decision flows, tests them, and publishes them as batch, API or real-time decision services that choose the next best action.

Model registry and MLOps for SAS, Python and R models: versions and validates models, deploys them to cloud, on-premises, database and container targets without recoding, monitors data, concept and model drift with alerts, and produces model cards for fairness and transparency.

Browser-based low-code machine-learning workspace in SAS Viya: build, compare and deploy predictive models as visual pipelines with AutoML for feature engineering, tuning and model selection, built-in interpretability reports, and SAS, Python or R code where needed.

Cloud-native data and AI platform for regulated enterprises: data access and governance, visual and code-based machine learning, forecasting, text analytics and optimization, model management and decisioning, run in the cloud, hybrid or on premises.

Generative-AI assistant for industrial engineers that answers equipment-troubleshooting questions by chat, generates and debugs automation code, and supports digital-twin simulation, built with Microsoft Azure AI.

Ready-to-use AI visual quality inspection system that trains on roughly 20 good samples to detect anomalies, deviations and missing parts on a production line without machine-vision or AI expertise.

Cloud predictive-maintenance software that applies industrial AI to machine and process data to forecast asset failures, rank risk across sites and prioritise maintenance work.

Generative and agentic AI system for Siemens EDA tools that ingests and vectorises multimodal design data, exposes a natural-language interface across the design workflow and automates debugging, with customer-choice LLM support.

Edge software suite for packaging, deploying, running and monitoring AI models on the factory floor, comprising an AI SDK, AI Inference Server and AI Asset Manager running on Siemens Industrial Edge and NVIDIA-accelerated industrial PCs.

Railigent X combines operational and condition data to detect rail asset faults.

Visual drag-and-drop and notebook-based platform in the Rapidminer portfolio for building, training and explaining machine learning and generative AI models, with AutoML tooling and deployment to cloud, on-premises or edge infrastructure.

Enterprise knowledge-graph platform in the Rapidminer portfolio, built on open W3C standards (RDF, SPARQL, OWL, SHACL), that federates cross-domain data into a governed semantic layer and gives AI agents a queryable, traceable context for grounded answers.