Popular AI Assistants Can Now Track Physical Inventory for Businesses
Lake Oswego, Friday, 10 July 2026.
EveryPoint has integrated its Stockpile Reports with major AI assistants, allowing businesses to track and measure physical inventory in real time using simple natural language queries.
Bridging the Gap Between Digital and Physical AI
On July 10, 2026, Redmond, Washington-based EveryPoint—a privately held company [GPT]—announced the beta launch of its Stockpile Reports Model Context Protocol (MCP) [1]. This development allows major AI assistants, including Anthropic’s Claude, OpenAI’s ChatGPT, Google’s Gemini, and Microsoft’s Copilot, to directly call and retrieve verified physical stockpile data [1]. By integrating these popular platforms, EveryPoint is transitioning bulk inventory management into the era of the ‘agentic supply chain,’ where autonomous AI agents can seamlessly interact with real-world assets [1].
How the Model Context Protocol Enables Real-Time Queries
The technology is built on the Model Context Protocol (MCP), an open standard developed by Anthropic [1]. This protocol functions as a secure bridge, allowing AI models to query specific, highly granular data points such as volume, tonnage, geographic location, product type, temporal changes, and coverage-confidence scores of physical stockpiles [1]. Instead of manually cross-referencing spreadsheets or static databases, enterprise managers and logistics coordinators can now use natural language prompts to retrieve real-time, verified inventory metrics directly through their preferred AI chat interfaces [1].
The Evolution of Physical AI and Spatial Computing
This integration highlights the growing significance of ‘Physical AI,’ which EveryPoint defines as artificial intelligence that perceives and measures the physical world rather than operating solely within digital text or on-screen data [2]. Unlike generative AI, which creates novel content from text prompts, Physical AI begins with the physical environment itself [2]. It utilizes cameras, drones, or smartphones to capture a scene, abstracts it into a structured model, and outputs actionable decisions or measurements [2]. This capability aligns with NVIDIA’s framing of Physical AI at CES 2025 as systems that can ‘perceive, reason, plan, and act’ in the real world [2], as well as venture capital insights emphasizing that machines operating in the physical environment require visual ‘eyes’ to function [2].
A Decade of Data and Proprietary Technology
EveryPoint has been developing its computer vision and machine learning models since 2011, with its Stockpile Reports solution active since 2012 [1][2]. Over this period, the company has compiled an extensive data repository consisting of more than 5 million individual measurements across hundreds of thousands of distinct stockpiles [1][2]. This repository represents more than 14 billion tons of measured material [1][2]. Based on these figures, EveryPoint’s systems have processed an average of 2800 tons of material per registered measurement. To protect its predictive, physical AI technology, which extracts volume and tonnage from ordinary imagery at the edge, EveryPoint holds 13 granted patents [1][2].
Driving Efficiency in the ‘Agentic Supply Chain’
The ability to easily summon verified inventory metrics is expected to significantly reduce manual auditing overhead and accelerate decision-making across the mining, construction, and bulk logistics sectors [1]. David Boardman, CEO of EveryPoint, emphasized the strategic advantage of this transition, noting that as supply chains become increasingly agentic, the businesses that succeed will be those whose real-world data is both trustworthy and proprietary [1]. By transforming physical imagery into reliable measurements, the MCP integration ensures that autonomous AI agents operate on verified facts rather than estimations [1][2].
Accessing the Beta and Future Outlook
As of the beta release on July 9, 2026, existing Stockpile Reports Business customers can opt-in to the integration directly through their accounts [1]. New users interested in leveraging AI assistants for physical inventory tracking are being directed to join a waitlist on the official website, stockpilereports.ai [1]. This rollout marks a practical step toward autonomous logistics, demonstrating how legacy industries can utilize open AI standards to bridge the gap between physical operations and digital intelligence [1][2].