Limble, the modern maintenance and asset management platform, today announced its Winter Release with three new AI-powered capabilities designed to help maintenance and operations teams build cleaner data, clearer asset plans, and faster workflows. The release focuses on AI that empowers the humans who keep the world’s assets running, applying it to help streamline asset data creation, improve planning, and safely integrate maintenance data with enterprise systems and AI tools.

“We have always prioritized solving the real, day-to-day problems that leaders and their teams face across operations and asset management,” said Michael Scappa, SVP of Product and Technology at Limble. “Our customers consistently say that AI is only important if it is saving them and their teams time as they work through maintenance, operations, and asset planning. This release applies AI where it matters most: lowering the burden on maintenance and operations teams while creating clean, reliable data and insights that extend the lifecycle of assets.”

The Winter Release expands Limble’s platform at the intersection of computerized maintenance management systems (CMMS) and enterprise asset management (EAM) with the following capabilities:

• Asset Snap automates asset creation by turning photos of machinery and equipment in manufacturing lines or facilities into structured, validated asset records in Limble. Using AI-powered image and text recognition, Asset Snap extracts and standardizes key details such as manufacturer, model, and serial number at the time of capture, helping teams onboard new and legacy equipment up to 80% faster.* At the same time, it eliminates manual entry, one of the most common sources of data errors in maintenance systems, resulting in more trustworthy asset databases that support accurate reporting, audits, and proactive maintenance planning. 
• Resource Planning adds AI-powered workload and scheduling recommendations and provides maintenance leaders with a single, real-time view of both scheduled and on-demand work. Based on internal tests of similar workflows, teams can expect to save 10-15 hours per week on scheduling, along with improved predictability and capacity visibility. With Resource Planning, leaders can clearly see what’s urgent or at risk, to allocate resources and balance workloads more effectively.
• Model Context Protocol (MCP) connects Limble to enterprise systems and AI tools, enabling secure access to trusted maintenance data for deeper insights and faster business decisions. For developers, MCP provides a standardized, secure way to integrate Limble data into AI clients like Cursor and Claude Code, accelerating integration and reporting workflows. For reliability engineers, asset planners, and maintenance leaders, MCP enables access to unique data and insights, directly through these LLMs and other AI tools. Using these insights to answer questions—such as which assets drive maintenance costs or where technician capacity is constrained—helps improve both daily operations and the decisions driving the lifecycle of assets.

The Winter Release reflects Limble’s continued commitment to empowering the humans who keep the world’s assets running while addressing industry-wide challenges where rising costs and skilled labor shortages push teams to do more with fewer resources.** Over the coming year, Limble will continue expanding its AI and automation capabilities, building tools that save time for the heroes who maintain equipment and facilities while keeping assets performing at their best.

Availability
All Winter Release features are available to Limble customers in the U.S. today, with a global rollout planned for completion in the summer of 2026. More information: https://limble.com/winter-release/

*Based on Limble’s internal estimates and data.

**1.9 million manufacturing jobs could remain unfilled by 2030 due to the skills gap (Source: 2024 Deloitte and The Manufacturing Institute Talent Study).