Amazon vs SAS

Amazon's AI shopping assistant in the Amazon Shopping app and on Amazon.com: answers shopping questions in the search bar, builds shopping guides and product comparisons, shows price history, and automates deal-finding and cart-building from the user's preferences.

Amazon's generative-AI Alexa: a conversational assistant across Echo devices and the Alexa app that handles multi-step requests, remembers context and acts on behalf of the user.

An assistant that provides conversational answers and guidance to procurement teams.

Amazon Connect Decisions provides supply chain planning and intelligence with AI teammates.

Amazon Connect Talent uses AI teammates to assess and interview candidates.

An operations service that uses machine learning to detect abnormal application behavior.

A component that lets users build workflows to automate repetitive tasks.

Business-intelligence part of Amazon Quick: dashboards, charts and embedded analytics over a company's data sources, with natural-language what-if analysis and agent-assisted exploration.

An application for building and training machine learning models without writing code.

Agentic AI for healthcare providers and health-tech developers: patient verification, appointment management, ambient clinical documentation, patient insights and medical coding, delivered inside Amazon Connect Customer for patient engagement and via an SDK at the point of care.

An AWS platform for building and running AI agents that automate browser-based UI workflows, billed per agent-hour of active work.

AWS's subscription AI work assistant combining chat-based research, business-intelligence dashboards and workflow automation, positioned as the successor to Amazon Q Business.

An application that creates automations from natural language or documentation using specialized AI agents.

A developer tool that identifies expensive lines of application code through runtime analysis.

An AWS coding assistant that generates, reviews, tests and autonomously modifies code inside IDEs and the CLI.

An AutoML component that automatically creates and selects machine learning models.

A component for preparing data, engineering features, and detecting data quality issues for machine learning.

A machine learning hub for evaluating, comparing, and selecting foundation models.

A serverless service for orchestrating and monitoring repeatable machine learning workflows.

A web interface for developing, training, evaluating, and deploying AI models.

Agentic AI development platform (IDE, CLI, web, mobile) that turns prompts into executable specs, validates code with property-based testing and runs parallel agents across large codebases.

A security platform that discovers, prioritizes, validates, and remediates software security risks.

Agentic-AI enterprise IT transformation workbench that accelerates cloud migration, application modernization (mainframe, Windows, custom code) and continuous tech-debt reduction.

An AI advertising platform that unifies omnichannel advertising and automates campaign execution and optimization.

Production AI-agent operational platform (framework- and model-agnostic) providing security controls, tool integration, debugging and evaluation for deployed agents.

Cloud contact-centre service, formerly simply 'Amazon Connect', now positioned as an agentic-AI customer-experience solution: AI agents and intelligent routing across voice, chat, email, SMS, web and WhatsApp, designed on a no-code canvas, with real-time performance tracking for agents and supervisors.

Amazon Nova Forge is a service for building custom frontier models using Nova and proprietary training data.

AWS's unified platform for building, training and deploying machine learning and generative AI models.

Single browser-based environment for data and AI development: find governed data across an organisation, then query it with SQL, work in serverless notebooks with a built-in AI agent, train models and build generative AI applications in shared projects.

Infrastructure for training, fine-tuning, serving, and observing models.

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.

SAS Event Stream Processing applies streaming analytics and machine learning to continuously flowing events.