ScaleOut Software Adds Live Digital Twin Data to Claude AI in Version 7 Release

ScaleOut Software integrates live digital twin telemetry with Claude Desktop

ScaleOut Software released Version 7 of its ScaleOut Product Suite on September 10, 2026, adding native integration between Claude Desktop and live digital twin data through the Model Context Protocol. The release positions ScaleOut as a vendor-neutral alternative to hyperscaler digital twin stacks for enterprises running generative AI on operational telemetry.

The new capability matters because it solves a specific problem: most digital twin platforms feed AI models historical data or require cloud migrations to access real-time state. ScaleOut's in-memory data layer connects Claude directly to live telemetry, system structure, and contextual metadata from running digital twins without routing through AWS IoT TwinMaker, Azure Digital Twins, or IBM watsonx.

Technical architecture and competitive positioning

Version 7 provides a fast, scalable in-memory data layer explicitly designed for retrieval-augmented generation over live digital twin models. Claude Desktop securely accesses this data via MCP, enabling real-time analysis of operational systems during incidents or optimization work.

This competes directly with Azure Digital Twins paired with Azure OpenAI and AWS IoT TwinMaker with Amazon Bedrock. The difference: ScaleOut runs on-premises or in any cloud, and its native Claude integration matters for enterprises standardizing on Anthropic models rather than OpenAI or proprietary hyperscaler AI services.

Siemens Digital Twin Composer on NVIDIA Omniverse and IBM watsonx offer broader platform capabilities but lack the real-time, in-memory specialization ScaleOut provides. For buyers prioritizing live operational data over simulation or historical analytics, ScaleOut introduces a new architecture pattern—a dedicated state store sitting between IoT platforms and AI copilots.

Budget and procurement implications

OT and data platform teams now face a choice: accept hyperscaler digital twin services with slower real-time performance and cloud lock-in, or add specialized infrastructure like ScaleOut for use cases where milliseconds matter.

Pilot budgets will likely land in the low-to-mid six figures for enterprises testing real-time GenAI on plant or fleet data. ScaleOut's architecture adds an independent layer alongside existing IoT platforms rather than replacing them, which reduces migration risk but increases infrastructure complexity.

Buyers evaluating digital twin platforms should benchmark hyperscaler offerings against ScaleOut's MCP-based, live twin integration. Ask Azure and AWS to match unified twin and real-time cache interfaces in a single console—a feature ScaleOut added in Version 7.

Digital Twin Consortium launches COMPOSE testbed with NTT DATA

The Digital Twin Consortium and NTT DATA announced COMPOSE—Composable Operational Microfactory Platform for Smart Enterprises—a new testbed led by NTT DATA Chile. Initial partners include Aingura IIoT, XMPro, Crysp, and Rowan University.

COMPOSE provides a reference architecture for combining industrial IoT, analytics, and digital twins across vendors. Each partner contributes specific capabilities: Aingura IIoT handles industrial IoT and analytics for heavy industry, XMPro provides event intelligence for manufacturing, Crysp adds AI analytics, and Rowan University supplies academic validation.

This positions NTT DATA's ecosystem against vertically integrated platforms from Siemens (Digital Twin Composer, Xcelerator), Hexagon, and AVEVA/Schneider Electric, which emphasize unified software suites over open testbeds.

For CIOs and OT leaders in manufacturing and logistics, COMPOSE offers a reference testbed for validating multi-vendor IoT analytics and digital twin stacks before committing to large rollouts. Budget impact: expect spend to shift toward ecosystem-friendly platforms like XMPro and Aingura IIoT that align with COMPOSE's architecture. Buyers gain leverage to insist on DTC-aligned interoperability in RFPs, forcing vendors to support consortium patterns or lose deals.

ASUS targets AI infrastructure with digital twin platform

ASUS announced an AI Factory Platform using the NVIDIA DSX Sim Blueprint to help enterprises design, simulate, validate, and optimize complete AI factories before physical deployment. The platform creates a unified digital twin environment for data center-class AI infrastructure, covering compute, networking, storage, power, cooling, and facility systems.

This matters for enterprises planning AI infrastructure buildouts who need to model thermal, power, and network constraints before purchasing hardware. ASUS competes with traditional data center planning tools and hyperscaler capacity planning services by offering a simulation-first approach tied to NVIDIA's ecosystem.

What to watch

Track whether hyperscalers respond to ScaleOut's MCP integration with their own real-time AI copilot features for digital twins. If they don't, ScaleOut carves out a defensible niche in operational AI use cases. Monitor whether COMPOSE testbed participants win multi-vendor digital twin deals, signaling that consortium-backed architectures gain traction over proprietary stacks. For AI infrastructure buyers, watch whether simulation-first planning using digital twins becomes a standard procurement step, reducing costly hardware missteps.