IBM vs SAS

IBM Automation Document Processing combines AI and deep learning to classify documents and extract information.

Protects enterprise data across AI agents, applications and endpoints against agentic-AI, regulatory and post-quantum risks.

An iOS and iPadOS application for labeling images and starting AI training jobs.

Full-stack application and infrastructure observability powered by agentic AI, including automatic discovery/mapping of GenAI workflows and AI-driven Smart Alerts to reduce alert fatigue.

A managed endpoint detection and response offering that combines AI automation, machine learning and human analysis for continuous threat detection and response.

AI-driven robotic process automation for building intelligent bots that automate business processes, with AI-embedded resilience built into the RPA runtime.

Visual data-science and machine-learning platform for building predictive models, with advanced algorithms, model deployment and open-source framework integration.

IBM Trusteer Pinpoint Assure assesses fraudulent intent and detects false or synthetic identities.

IBM Trusteer Pinpoint Detect provides real-time risk scoring and detects credential-theft malware.

Document intelligence platform using OCR, NLP enrichments (entity/sentiment/emotion/category extraction) and LLM integration to automate information discovery from enterprise documents.

A conversational AI platform for building customer-support chatbots and virtual assistants deployed across voice and digital channels.

Platform for building and running AI agents that call enterprise applications on a user's behalf.

An AI coding agent that automates planning, code generation, review and verification across the software development lifecycle, including IBM Z mainframe modernization workflows.

A generative AI assistant for IBM Z mainframe application discovery, code generation, refactoring and modernization.

IBM Cognos Analytics provides business analytics and agentic AI with natural-language data exploration.

An agentic AI IT-operations platform that unifies observability, optimization and automated remediation across applications, infrastructure and networks.

IBM Maximo Application Suite combines asset maintenance, inspections and reliability management with image and video analysis.

IBM Planning Analytics combines business planning with AI guidance and demand forecasting.

IBM Process Mining visualizes workflows and analyzes process data to identify inefficiencies.

IBM Turbonomic uses AI to optimize cloud resources and automate operational decisions and actions.

Studio for building, tuning and deploying model-based applications on IBM and third-party foundation models.

IBM watsonx.data integration automatically generates, validates and executes data pipeline plans.

A governance platform providing real-time visibility, policy enforcement and accountability tracking for AI models and agents across the AI lifecycle.

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.

No-code retrieval-augmented generation and agent builder: ingests unstructured enterprise documents with multilingual OCR, answers questions with citations through a choice of third-party LLMs and vector databases, evaluates the answers, and runs goal-driven agents, on premises or in the cloud.

Self-provisioned cloud coding environment for building analytic, machine-learning and AI models in SAS, Python or R from Jupyter, VS Code or SAS Enterprise Guide, with chosen CPU, memory and GPU sizes and sessions that shut themselves down.

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.