AI agents don’t sleep. They operate continuously—querying live data, triggering workflows, and making recommendations at machine speed. But most enterprises are trying to run this always-on reality on platforms designed for a different era: economics designed for ephemeral workloads, stitched stacks, duplicated data across warehouses, and governance bolted on after the fact. The result is predictable—pilots that never scale, costs that surprise, and risk that grows as quickly as innovation.
That’s why we built the Teradata Autonomous Knowledge Platform: a unified, AI-native foundation designed for continuous, production-grade intelligence—where analytics, AI, and operational decisioning converge on governed enterprise data.
Autonomous knowledge means turning enterprise data into governed, trusted understanding so AI has the context it needs to sense, decide, act, and adapt across the enterprise in real time. It brings AI and knowledge together—so your knowledge itself becomes dynamic, context-aware, and capable of driving outcomes across the business.
Today, I’m excited to introduce Teradata Factory, the newest component of the Teradata Autonomous Knowledge Platform and our purpose-built, ready-to-run, on-premises system for organizations that require data sovereignty, private AI, and predictable economics.
AI and enterprise knowledge belong together
Enterprises know AI requires high-quality data—and lots of it. Yet many are held back by massive tech debt and unmanaged data sprawl, which forces expensive replication and movement across silos. At the same time, AI is moving directly into the core data platform: teams want to run large language model (LLM) inference, vector search, and agentic workflows right next to the data that already powers mission-critical analytics.
This convergence creates “new physics” for the platform. Always-on agents introduce nonstop execution, massive query volumes, and mixed workloads that compete for resources. Traditional architectures weren’t designed to protect analytical SLAs while simultaneously supporting GPU-heavy AI experimentation and production inference. Many organizations try to bridge the gap by assembling a multi-vendor stack—enterprise data warehouse (EDW), lakehouse, GPU infrastructure, orchestration, governance, and tooling—then hoping it behaves like one system. But DIY integration slows innovation, increases operational burden, and expands risk exactly when trust and compliance matter most.
Lakehouse, EDW, and AI/GPU: They’re all here
Teradata Factory answers the challenges of this new era. It is a fully integrated, ready-to-run, on-premises system designed to run enterprise analytics, lakehouse workloads, and AI side by side—close to governed enterprise data. In short: one integrated system, delivered as a Teradata-engineered platform built on proven enterprise infrastructure, so customers can operationalize outcomes without assembling the stack themselves.
Teradata Factory extends the same Autonomous Knowledge Platform architecture across deployments. Whether customers run in public cloud, on premises, in sovereign cloud, or in hybrid environments, the goal stays consistent: AI agents query live data in place—no copying, no movement, no added risk. Agents go to the data, and the data stays put. With Teradata Cloud and Teradata Factory, organizations can choose where to deploy without changing their architecture, tooling, or governance model:
- Sovereignty, without compromise: Run AI where governed enterprise data lives—under full regulatory and operational control
- Analytics and AI, without disruption: Protect mission-critical performance with mixed-workload management and SLA safeguards built in
- Modular design, without overbuild: Start with purpose-built configurations for EDW, lakehouse, and/or AI workloads—available in multiple sizes and expandable as demands grow
- One system, without tool sprawl: Eliminate stitched platforms with a single control plane across EDW, lakehouse, and AI/GPU workloads—hybrid-ready by design
Inside Teradata Factory, the stack is integrated end-to-end: a single management plane for access, governance, policy, and workload control; Teradata Database for enterprise-grade analytics; and Teradata AI Studio to build, deploy, and operationalize analytics, AI, and agents in one governed experience. Underneath is Dell’s enterprise-grade infrastructure foundation—compute and storage designed for mixed analytics and AI workloads—so customers get a turnkey system instead of a DIY integration project. Add NVIDIA AI Enterprise software, NVIDIA AI Infrastructure, and high-performance networking, and Teradata Factory becomes a practical, production-ready way to run AI side by side with mission-critical analytics on a single engineered platform.
Just as importantly, Teradata Factory is designed for adoption curves in the real world. Customers can begin with an EDW and lakehouse foundation, then add GPU acceleration when—and if—they’re ready for AI. By scaling CPUs and GPUs independently, organizations avoid forced up-front investment while still gaining a path to production-grade generative AI and agentic workloads, governed alongside the analytics that already run the business.
Enabling valuable outcomes for highly regulated industries
We’re seeing strong interest from regulated industries and organizations operating in hybrid environments—especially where data residency, auditability, and predictable economics are nonnegotiable. Here are a few common scenarios that come up when teams move beyond pilots and into always-on execution:
- Healthcare payers: Teams want to run LLMs directly on claims data to classify and predict denials, reduce administrative waste, and improve processes like prior authorization—without moving sensitive data off platform. They also want governed pipelines that combine structured and unstructured data for risk adjustment and Healthcare Effectiveness Data and Information Set (HEDIS) quality measurement.
- Financial services: Banks exploring “an AI agent for every customer” need continuous monitoring, next-best-action analytics, and scalable model operations across large customer bases. Teradata Factory’s modular CPU/GPU scale supports training and inference while keeping customer data under the bank’s control and governance model.
- Logistics and supply chain: Organizations modernizing route planning and rerouting want agentic decision support that evaluates cost, schedule, and fuel impacts before action. These scenarios combine machine learning (ML), knowledge models, and geospatial or graph analytics—best delivered when analytics and AI share the same governed data foundation.
- Defense and intelligence: In environments where latency and control matter, teams are exploring multi-modal AI that can analyze imagery and provide real-time guidance—supported by continuous model updates, operational analytics, and vector-native retrieval. These missions demand an engineered system that can run under strict governance while sustaining always-on execution.
Across all these examples, the conversation is the same: we’re not optimizing for hardware specs—we’re enabling outcomes. Teradata Factory makes it practical to run analytics, vectors, models, and agents together in a controlled environment, so teams can move from experimentation to production without the operational drag of stitching platforms together.
Technology that meets the moment
Agentic AI is changing the operating model of the enterprise. When intelligence becomes continuous, the platform underneath must be continuous too—governed, resilient, and built to protect performance even as AI workloads scale. The Teradata Autonomous Knowledge Platform was designed for that moment, and Teradata Factory is the latest step in making it real for customers who need on-premises deployment; data and AI sovereignty; and private AI by design. And because Teradata Factory is delivered on proven Dell enterprise infrastructure—with modular design of compute and storage engineered for predictable scaling—it brings a practical path to production without asking teams to stitch together and operate a fragile, multi-vendor stack.
If you’re evaluating how to move from AI pilots to always-on execution—without duplicating data, compromising governance, or stitching together five vendors—let’s talk. Connect with your Teradata team to explore where Teradata Factory fits in your roadmap, see how the integrated Dell and NVIDIA foundation supports advanced AI workloads with enterprise-grade performance, and discuss how Teradata Factory and Teradata Cloud can give you deployment choice without architectural trade-offs.