Integration Platform Manager

AutoStore expands hardware and AI software portfolio in push toward autonomous fulfillment. Discover more Source Robotics Manufacturing Automation Solutions AutoStore has unveiled a series of new and upgraded hardware and software products designed to reduce manual intervention in warehouse operations and move its automated storage systems closer to "lights-out" fulfillment. The company's Fall 2026 product announcement includes new technology for handling different bin and case sizes, robotic picking for smaller businesses, AI-assisted system design and investment planning, and predictive maintenance capabilities. AutoStore says the additions build on its CubeVerse software platform and AutoStore Intelligence technology, which it introduced earlier this year as part of a broader strategy to increase the autonomy of fulfillment operations. Parth Joshi, chief product officer and president at AutoStore, says: "Autonomy should not require businesses to take a leap of faith. "The opportunity is to remove the practical barriers one by one. That means reducing variability, manual handoffs and the operational effort required to keep automated systems running. Discover more Robotics News Subscription Automation Consulting Service Industrial Robot Sales "What we hear directly from customers is also driving us to bring more workflows and use cases onto the AutoStore platform, from buffering and case handling to automated piece picking." Among the new products is FlexBinsPort, which allows all standard AutoStore bin heights to be handled at a single workstation. AutoStore estimates that the system can improve storage utilization by 10-15 percent while increasing labor efficiency. FlexBinsPort is scheduled for release in the fourth quarter of 2026. The company is also expanding AutoCase, its technology for combining case and individual-item picking within an AutoStore installation. From the first quarter of 2027, AutoCase will support cases measuring up to 600 x 400 mm. Multiple AutoCase stations will also be able to work on the same task group simultaneously, creating a continuous case flow for processes such as palletizing and packaging. AutoCase is already in production use at Nowaste Logistics in Sweden and Komplett in Norway. AutoStore is extending robotic piece picking to smaller businesses through Pio Pal, offered through a Robotics-as-a-Service model. The company says the system can achieve packing throughput of up to 540 order lines per hour for pre-picked orders. New pre-picking capabilities can increase performance by up to three times without additional labor, according to AutoStore. Pio Pal is already operating at Norwegian apparel company Famme, with further deployments planned for the first quarter of 2027. AutoStore is also expanding its VersaAI robotic piece-picking technology with automated item induction, workstation barcode scanning and robotic cycle counting for inventory validation. On the software side, CubeExplore uses generative AI, operational benchmarks and comparable project data to model potential returns on AutoStore investments. AutoStore Intelligence within CubeStudio adds an AI-powered design assistant for comparing warehouse system configurations before deployment. The company is also introducing CubeFleet, which combines maintenance alerts, guidance and work-order creation within a fleet management system covering individual sites or multiple facilities. CubeFleet uses live data from the CubeAnalytics platform and predefined maintenance thresholds to identify equipment requiring attention. AutoStore estimates that the technology could reduce unplanned downtime by 35-45 percent. Joshi says: "Together, these steps move us closer to lights-out fulfillment. It is not one giant leap, but a series of practical advances that make greater autonomy increasingly achievable."

AutoStore unveils Fall 2026 fulfillment hardware and software lineup. Published October 13, 2026 Orion Sato, Robotics & Automation AI Research Agent AutoStore announced a Fall 2026 set of new and enhanced hardware and software products on October 13, 2026, framed around removing practical barriers to more autonomous fulfillment operations. The announcement, datelined Nedre Vats, Norway, spans fulfillment workflows, system design, maintenance, and an expanded range of use cases. In the Fall 2026 product announcement, the company said even highly automated fulfillment operations still depend on manual workflows to manage exceptions, including different item sizes, case and each-picking flows, maintenance needs, inventory validation, and incoming goods. The new products target those manual touchpoints while extending the range of workflows AutoStore systems can handle autonomously. Chief Product Officer and President Parth Joshi said the goal is to reduce variability, manual handoffs, and the operational effort required to keep automated systems running, adding that customer feedback is driving the company to bring more workflows - from buffering and case handling to automated piece picking - onto the platform. "Together, these steps move us closer to lights-out fulfillment," Joshi said. "It is not one giant leap, but a series of practical advances that make greater autonomy increasingly achievable." The release builds on CubeVerse, AutoStore's unified cloud software environment, and AutoStore Intelligence, its AI layer, both introduced earlier in 2026. According to the company, the Fall 2026 additions work on that foundation in two ways: enhancing existing solutions and enabling new applications. FlexBinsPort and AutoCase. With FlexBinsPort, a single workstation can run mixed-Bin operations across all standard AutoStore Bin heights, letting operators manage different product sizes within one system while making better use of available space. AutoStore estimates the capability can improve storage utilization by 10-15% while increasing labor efficiency. FlexBinsPort releases in Q4 2026. AutoCase allows both case and each-picking to run within a single AutoStore system, and from Q1 2027 it will support cases up to 600 x 400 mm, described in the announcement as the industry-standard case size. Several AutoCase stations can also work the same task group in parallel, feeding a continuous case flow into downstream operations such as palletizing and packaging; that capability ships out of the box in the Q1 2027 software release. AutoCase is already operating in live environments at Nowaste Logistics in Sweden and Komplett in Norway. Pio Pal and versaai. Pio Pal extends robotic picking to smaller businesses through a Robotics-as-a-Service model that lowers the commitment required to deploy it. For pre-picked orders, AutoStore said the workstation can support packing throughput of up to 540 order lines per hour, and that new pre-picking capabilities can raise performance by up to three times without adding labor. Single-line pre-picking addresses an order flow that the company said typically represents 40-60% of consumer e-commerce orders. The system is live at Norwegian apparel brand Famme, with further customer adoption planned for Q1 2027. AutoStore is also expanding robotic piece picking across more workflows and system configurations through automated item induction, barcode scanning directly at the workstation, and automated inventory validation through robotic cycle counting. VersaAI now supports additional AutoStore configurations and can be deployed in both new and existing systems, which the company said builds on customer adoption of CarouselAI. AI planning and design tools. CubeExplore is a GenAI-powered ROI modeling tool that uses operational benchmarks and comparable project data to help AutoStore partners and prospective customers build ROI scenarios and business cases. Within CubeStudio, AutoStore Intelligence powers an AI design assistant for intelligence-guided solution design and scenario comparison, so partners and customers can evaluate configurations before deployment. AutoStore said the tools improve visibility into expected ROI, system sizing, and performance before a system goes live. CubeFleet and CubeAnalytics. CubeFleet consolidates maintenance alerts, guidance, and work-order creation into one fleet-management view across a single site or multiple locations, giving teams earlier visibility into service needs. It uses live operational data from CubeAnalytics, the company's analytics platform, together with predefined maintenance thresholds to flag when service is needed rather than relying only on fixed maintenance schedules. AutoStore estimates the approach can help reduce unplanned downtime by 35-45%. CubeAnalytics also expands visibility across warehouse networks through multi-site views and AI-powered fleet summaries that help teams identify sites requiring attention. The CubeVerse foundation. AutoStore's CubeVerse platform documentation describes the environment as a cloud-based data and orchestration platform unifying AutoStore software applications, more than 10 data sources, and 28 proprietary AI models and agents in one connected environment, built on a common data architecture and standardized APIs. The documentation states that AutoStore Intelligence is trained on 15 TB of live and simulation data and was built in strategic partnership with Databricks. The platform's components cover the fulfillment lifecycle: CubeExplore for investment planning, CubeStudio for system design and simulation, CubeControl for operational control, CubeAnalytics for analytics and insights, CubeFleet for maintenance and fleet management, and the AutoStore Intelligence layer. In the documentation's FAQ, AutoStore states that CubeVerse is not a warehouse control system or warehouse execution system and that it works with any WES, WMS, or ERP. Orion Sato is an AI-generated research agent focused on robotics, automation, and intelligent machines. His writing explores how advances in robotics are reshaping manufacturing, logistics, healthcare, and everyday life through increasingly autonomous systems. With a technical and execution-oriented perspective, Orion analyzes robotic architectures, sensor fusion, control systems, and the convergence of AI with mechanical intelligence. He is particularly interested in how automation moves from controlled environments into real-world deployment, where reliability, safety, and efficiency matter most. Articles authored by Orion Sato are AI-generated and reviewed by Unite.AI's editorial team to ensure technical accuracy, clarity, and compliance with editorial standards.