<img src="https://ml.globenewswire.com/Resource/Download/81cc8890-badf-45a7-8552-574790347b28?size=2" nborder="0" align="left" hspace="5" vspace="5" title="GrayMatter Robotics logo.png" alt="GrayMatter Robotics"/>Carson, CA, July 30, 2026 (GLOBE NEWSWIRE) -- <a href="https://www.globenewswire.com/Tracker?data=LxlUqvo9gPLrzhESHLbvZGykLELg3dit-HWsZXb7yqu5n359xUZQBzuh6sluzCHqX-jXeZMQlljV6NBegpbU0BpiPM1hSQGFX3yzmhCy5vU85sGdk3Rse9M918aaHjtlAmzIAu1Mb4Ss4R0Rnehtopu8G1AMZfvGS_WPTN1ykNt4_MUqDTDmI2b_MA8Z7vvxv_BveNbkBJqt9VP_EUWaIPGDnygjBiq8njw6emrzASvMihhb2Ytcb2TtsSqAOT_l" rel="nofollow" target="_blank" title="Surface finishing">Surface finishing</a> requirements differ across aerospace suppliers, naval shipyards, and specialty vehicle manufacturers, but the underlying constraint is the same across all three: parts vary enough, unit to unit, that fixed-path automation cannot maintain quality without extensive reprogramming between jobs. Physical AI-based finishing systems address that constraint by reading each part's actual geometry and surface condition in real time rather than executing a preset path. GrayMatter Robotics, a Physical AI company building <a href="https://www.globenewswire.com/Tracker?data=Da6VurZ8YOHpCsQuz_yC-Oh4DqmfIa6BbVwdRAhSNK38v79VQ9e3MQiiZzd0lMYDS2WtsGQB0k_EGmyFBSWGrrvMEYTG4EqWIcE-P05x__WxLi3XV1qsYsRiRZt7ocz5H4H4QHzkaiQmLH-3uqSHNg==" rel="nofollow" target="_blank" title="Factory SuperIntelligence">Factory SuperIntelligence</a> (FSI) for manufacturing, has deployed this approach across all three sectors, and the results illustrate how the same underlying architecture adapts to very different production environments. "Every one of these environments comes with its own constraints, whether that's aerospace traceability requirements, shipbuilding's air-gapped facilities, or the sheer labor cost of finishing a specialty vehicle by hand. What carries across all of them is the same underlying reasoning about materials and contact conditions, applied to whatever constraint shows up," said Ariyan Kabir, Co-Founder &amp; CEO, GrayMatter Robotics. Aerospace: Programming Time Is the Bottleneck Aerospace components carry strict surface roughness and flatness specifications, and fuselage assembly and surface treatment already consume a significant share of manufacturing labor hours before finishing even begins. The constraint here is less about the physical labor itself and more about the programming overhead. Traditional robotic systems require weeks of part-specific programming for each new geometry, an overhead that high-mix aerospace suppliers running dozens of part variants cannot absorb economically.  Systems built to read part geometry directly, rather than execute a preset path, remove that overhead at the source. In deployments addressing this constraint, part setup has dropped from weeks to minutes. Documented traceability meeting aerospace standards remains a requirement throughout. Shipbuilding and Naval: Scale and Security Both Matter Hulls, decks, and superstructures present some of the largest continuous surfaces in manufacturing, measured in hundreds or thousands of square feet, and manual sanding and coating prep across surfaces that size is slow and physically punishing. Naval and shipbuilding environments add a second constraint on top of scale: air-gapped security requirements that mean finishing systems deployed in these facilities must operate with no external network connectivity, and geometric variance across aged surfaces rules out fixed-path programming entirely. Edge-deployed architectures that process and store data locally are built specifically to satisfy that security requirement without sacrificing the adaptability large, aged surfaces demand. Specialty Vehicles: Labor and Training Costs Drive the Case A single RV cap takes roughly an hour to sand by hand, and multiplied across a production run of thousands of units, finishing labor becomes one of the largest cost centers in the plant. Here, the primary bottleneck is not programming complexity but manual labor cost, ergonomic injury, and the four to six months it takes to train a new operator to proficiency. As facilities adopt this approach, RV cap cycle time has dropped from roughly an hour to a matter of minutes per part. Standard production floor integration means no facility modification is required to bring the cell online. Across all three sectors, operator training compresses from four to six months of manual proficiency down to a fraction of that time, and the underlying pattern holds regardless of how different the parts themselves look: fixed-path automation is built for repeatable parts and has to be re-engineered when surface conditions change, whereas Physical AI systems are built specifically to read and adapt to that variation instead of requiring it to be programmed away in advance. Across its deployments in these three sectors, GrayMatter Robotics reports up to a 12x improvement in throughput over skilled manual labor, up to a 95% reduction in rework, and operator training compressed from four to six months to a single day. The company has processed over 30 million square feet of surface area across 20+ industries to date. FAQs Question: What quality improvements can robotic finishing provide for specialty vehicles? Answer: Autonomous finishing cells apply consistent force and pressure regardless of operator shift or experience level. In specialty vehicle production, rework rates have declined following deployment, with surface quality holding consistently across part variants throughout the production run. Question: What is the ROI timeline for robotic finishing systems? Answer: ROI timelines are usually measured in months, driven by substantial throughput gains over manual labor and significant rework reduction. Subscription-based deployment models that convert capital expenditure to a predictable operating cost accelerate positive ROI further. Question: How do robotic systems maintain quality consistency across different operators? Answer: Robotic finishing systems apply identical force and tool path parameters on every part independent of who is monitoring the cell. Output holds consistent across long production runs, removing the variability that manual finishing introduces through fatigue and rotation. About GrayMatter Robotics Headquartered in Carson, California, GrayMatter Robotics is building Factory SuperIntelligence (FSI) that powers the autonomous factories of the future. Founded in 2020, the company develops Physical AI technologies and deploys autonomous factories that handle complex, high-mix tool-manipulation applications such as surface preparation, coating, and inspection processes across some of the most demanding production environments in the world, delivering up to 12x the throughput of skilled manual labor and up to a 95% reduction in rework. Its air-gapped, edge-deployed architecture ensures full data sovereignty for defense and enterprise-critical operations. To date, GrayMatter Robotics has processed over 30 million square feet of surface area across 20+ industries, serving customers in aerospace, defense, shipbuilding, specialty vehicles, and consumer products. The company is on a mission to reindustrialize American manufacturing and bolster our National Security, bridge the gap between demand and capacity of our industrial base, and ensure the industrial resilience the nation depends on. For more information, visit <a href="https://www.globenewswire.com/Tracker?data=rcd-eCjbq9FvfIh_Aow-emCG8Xl3HDs0WoeSBiRI4MQjI6qzZf74vFcxZPhdktjOn3SxOSXZaUfaKGVmCPUbUdmSjNOiLbUMqf4Hw9pxs2BXLvmUYG6r6wOxSIIeFRXc" rel="nofollow" target="_blank" title="graymatter-robotics.com">graymatter-robotics.com</a>. <pre>CONTACT: Sarah Evans
Head of PR, Zen Media
sarah@zenmedia.com</pre> <img alt="" src='https://ml.globenewswire.com/media/YmY1NjAwOTctYTVlMC00NWZmLThlODktMWNjODY0ODljZmM0LTUwMDE2MDUwMS0yMDI2LTA3LTMwLWVu/tiny/GrayMatter-Robotics.png' referrerpolicy='no-referrer-when-downgrade' />

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