From insight to action

AI that understands your business—and executes in real time

Connect data, AI, and agents to sense, decide, and act—continuously, at enterprise scale with Teradata’s Autonomous Knowledge Platform.

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Illustration of core strengths built into Teradata's platform, resulting in lower costs, while seamlessly progressing AI/ML models from ideation to production.
A new way to run the enterprise

AI no longer stops at insight. It helps move decisions into action.

Enterprises need more than insight—the Teradata Autonomous Knowledge Platform unifies data, AI, and analytics into a governed system that enables agents to move from insight to continuous, reliable action at scale.

One system. Continuous intelligence.

A platform where intelligence doesn’t stop

The platform runs on two layers of autonomy: 

  • System-level autonomy handles the operational heavy lifting, automatically sizing, placing, andoptimizingworkloads to balance performance, cost, and governance

  • Knowledge-level autonomy keeps the intelligence sharp, continuously capturing meaning, relationships, and context so AIdoesn’tjust act—it acts with confidence
The Teradata difference

A platform where intelligence doesn’t stop

Teradata is built differently. Intelligence isn’t bolted together—it’s designed as a system from the ground up:

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Connected

A connected data foundation unifies structured and unstructured data across every environment.

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Agentic

Autonomous agents orchestrate decisions and execution in real time.

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Fit-for-purpose

Fit-for-purpose compute dynamically balances performance, cost, and workload demands.

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Hybrid

A hybrid operating model ensures consistency whether you're in the cloud, on premises, or navigating the strictest regulatory constraints.

Proven in production

From AI pilots to enterprise outcomes

Across industries, enterprises aren't experimenting with AI anymore. They’re running it in production, continuously and at scale, on the Teradata Autonomous Knowledge Platform:

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Financial

Financial institutions monitor and act on risk signals in real time.
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Healthcare

Healthcare organizations improve response times and operational efficiency.
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Retail

Retailers optimize pricing, promotions, and inventory dynamically.
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Manufacturing

Manufacturers predict and prevent failures across global operations.
Deployment options
See it in action

A practical path from experimentation to execution

From real-time fraud prevention to predictive operations and personalized customer experiences, Teradata moves AI out of the lab and into production, where it delivers impact that shows up in the numbers. 

Talk to an expert

FAQs

Category & foundations

Autonomous knowledge is the ability of an enterprise platform to turn raw structured and unstructured data into trusted, governed understanding. That understanding embeds business context—meaning, relationships, and lineage—so AI systems can reason and act reliably within enterprise policies and controls.

AI systems and agents operate continuously and at scale, requiring platforms built for high concurrency, governance, cost control, and operational reliability.

It’s a category that unifies data platforms, AI systems, and execution layers so intelligence can operate continuously—not just generate insights.

Autonomous knowledge is a new category because traditional platforms separate data, analytics, and AI, producing insights without operationalizing them. This category unifies data, AI, and execution so intelligence can continuously drive action, not just inform decisions.

Knowledge is data understood in business context—enriched with meaning and governance so it can drive decisions and actions.

Autonomy and knowledge work together by combining continuous system optimization with context-aware intelligence. Knowledge provides the meaning, relationships, and governance that ground AI systems, while autonomy ensures those systems can continuously operate, adapt, and execute actions at scale.

Platform overview

The Teradata platform continuously manages execution and improves knowledge without human intervention.

  • System-level autonomy: Automatically optimizes compute, performance, cost, and data placement based on workload demand—so workloads run efficiently without manual tuning
  • Knowledge-level autonomy: Continuously updates and refines enterprise knowledge by incorporating new data, context, and outcomes—so AI systems improve how they reason and act over time

Together, they enable continuous, self-optimizing intelligence. Systems don’t just generate insights. They operate, adapt, and act.

It’s a unified platform combining data, analytics, AI, and execution. In May 2026, it evolved from Teradata Vantage® and ClearScape Analytics®, now unified under a single architecture.

The platform is designed to allow customers to move from AI experimentation to enterprise production outcomes while maintaining security, governance, and cost control. It reduces fragmentation across data platforms and AI toolchains and minimizes data movement by providing a unified foundation for analytics and AI workloads, with governed execution.

It’s designed for machine-driven workloads—not just human interaction.

The platform supports a range of AI, lakehouse, and data engineering use cases, as well as other enterprise use cases, including:

  1. Retrieval-augmented generation (RAG)
  2. Fraud detection and risk modeling
  3. Customer intelligence and personalization
  4. Operational decision automation
  5. Multi-modal AI applications

The platform builds on Teradata’s enterprise software stack for AI and lakehouse workloads in an autonomous platform for scalable execution.

Most platforms specialize in data engineering, analytics, or AI development. Teradata differentiates by enabling continuous, governed execution at scale—combining data, AI, and operational decisioning in a single platform.

No. Existing Teradata customers can adopt new capabilities without fully re-architecting their current data, applications, or workflows.

Performance and cost vary by workload and environment. Teradata supports evaluation using real-world workloads, including side-by-side comparisons and migration-based validation to measure performance and efficiency.

The platform is designed for AI systems and agents—not just human users. It supports continuous, high-concurrency workloads where machines query, reason, and act at scale

The platform supports cloud, on-premises, and hybrid deployments, allowing organizations to choose the model that best fits requirements for scale, cost, data residency, and regulatory needs.

The platform uses feedback loops from workloads, outcomes, and usage patterns to continuously optimize performance, cost, governance, and execution—improving how intelligence operates over time.

The platform enables organizations to run mission-critical workloads where they need to, while scaling innovation across environments—without re-architecting systems or compromising governance.

Organizations can evaluate the platform using proof-of-value programs, real-world workloads, and architecture reviews—covering cloud, on-premises, and hybrid deployment scenarios.

  • AI Studio
    • AgentStack (part of AI Studio)
  • Enterprise Vector Store
  • Teradata Database
  • Connected Data Foundation
  • Teradata Fabric

Vector databases store embeddings. Enterprise Vector Store integrates vectors with structured data, governance, and workflows to provide AI and analytics accurate context.

Hybrid search combines semantic (vector) search with lexical (keyword) search to deliver more accurate, context aware results than either method alone, helping users find the right information across complex enterprise data sources.

Fusion search refers to retrieving insights across structured data (tables) and unstructured data (documents and other content) together, enabling richer context and better answers without requiring separate systems or complex stitching.

Enterprise Vector Store supports multi-modal unstructured formats including text, PDFs, images, audio, and video. Through NVIDIA and Unstructured integrations, preprocessing and embedding generation are automated for unified ingestion.

Teradata Enterprise Vector Store is an integrated, enterprise‑grade capability that manages, stores, and retrieves unstructured data using vector embeddings, enabling fast and intelligent search and retrieval as a trusted context layer for AI applications. Enterprise Vector Store enables hybrid search across structured and unstructured data within a governed environment.

Through APIs and integrations supporting embedding, indexing, retrieval, and RAG workflows.

The Connected Data Foundation is a unified data layer that provides consistent access to data across environments, formats, and platforms. It enables organizations to work with data wherever it resides—without duplication—while maintaining governance, performance, and scalability.

Teradata renamed its platform and products to use descriptive, plain-language names that reflect what the technology does, aligning with broader industry trends and improving clarity across the portfolio.

As of May 2026, the following Teradata product names have changed:

  • Teradata Vantage® → Teradata Autonomous Knowledge Platform
  • ClearScape Analytics®/AI Workbench → Teradata AI Studio
  • QueryGrid® → Teradata Fabric
  • Teradata VantageCloud → Teradata Cloud
  • IntelliFlex®/AI Factory → Teradata Factory