What is possible now that was not possible before?

Modern technical work is fragmented. Physics is separated from biology. Artificial intelligence is separated from manufacturing. Simulation is separated from test data. Engineering judgment remains trapped in individual minds. Company knowledge disappears when people leave. Research becomes papers without becoming buildable systems. Teams repeat failures because the reasoning behind earlier decisions was never preserved.

The tools may improve, but the institution forgets.

MNEOS exists to reverse that.

MNEOS is being built as a long-lived institution where scientists, engineers, builders, and intelligent systems work together to solve physical-world problems beyond the reach of any one company, discipline, tool, or founder.

It combines aspects of a research institute, engineering laboratory, technical residency, AI-systems organization, manufacturing-aware design environment, venture platform, and institutional memory system. It is not reducible to any one of them.

Computational engineering is the integrated practice of using computation to understand, design, optimize, manufacture, test, and deploy physical systems. It brings together physics-based simulation, AI-assisted reasoning, computational electromagnetics, computational biology, computational materials science, optimization and inverse design, scientific machine learning, robotics and embodied intelligence, digital manufacturing, experimental evidence, human engineering judgment, institutional memory, and governed decision systems.

Engineering becomes a continuous intelligence loop.

MNEOS does not begin from the premise that artificial intelligence should replace scientists or engineers.

Human beings remain essential for judgment, intuition, ethics, taste, mission understanding, physical experience, relationship trust, responsibility, and final authority. Artificial intelligence can expand search, synthesis, simulation support, pattern recognition, hypothesis generation, design exploration, code generation, anomaly detection, optimization, and documentation.

The collaboration must be governed.

Beneath MNEOS is DOS, the governed computational substrate that connects people, AI systems, documents, decisions, simulations, experiments, workflows, equipment, projects, relationships, evidence, and action.

DOS allows humans and artificial intelligence systems to collaborate across time without losing memory, context, authority, provenance, or accountability. It is not simply another assistant interface. It is the architecture through which MNEOS remembers, reasons, coordinates, verifies, and acts.

The next frontier is not only intelligence that generates language, images, or software. It is intelligence that understands physical constraints, interacts with tools and environments, collaborates safely with people, learns from real evidence, and contributes to building, testing, maintaining, and operating complex systems.

MNEOS intends to explore humanoid and mobile robotic systems, robotic laboratory assistants, automated experimentation, dexterous manipulation, robot-supported manufacturing, autonomous inspection, simulation-to-reality transfer, human-machine work allocation, safety, authority, and accountability in embodied AI, and long-duration learning from physical operations.

The goal is not spectacle. The goal is useful, governed physical intelligence.

Engineering intelligence is incomplete if it cannot move into physical reality. MNEOS will connect design and simulation to real manufacturing constraints, materials behavior, process evidence, inspection, qualification, and field performance.

Initial areas may include additive manufacturing, ceramic and advanced material systems, RF and microwave structures, multimaterial fabrication, digital manufacturing instructions, process simulation, robotic production, quality evidence, design for manufacturability, distributed and allied production, and qualification and certification support.

MacroVation and related operating environments provide practical settings for applying these ideas to real programs and hardware — subject to governed boundaries, permissions, confidentiality, export controls, and originator rights.

The MNEOS Engineering Commons is the long-term layer through which engineering knowledge compounds. It is not a claim that all knowledge is public, unowned, or freely transferable. It is an architecture for preserving and governing design rules, test evidence, simulation models, manufacturing knowledge, failed attempts, technical decisions, supplier and process knowledge, reusable architectures, people and capability networks, publications and training, research questions, and institutional lessons.

Ownership, permissions, confidentiality, security, export-control requirements, and originator rights must remain visible.

The next project should not begin where the last project began.

MNEOS is beginning to assemble a multidisciplinary community of scientists, engineers, mathematicians, researchers, software architects, machinists, builders, and technical leaders who want to work on consequential physical-world problems. We are especially interested in people who work across boundaries rather than inside only one discipline.

MNEOS is interested in thoughtful relationships with universities, national and government laboratories, research institutes, defense and aerospace organizations, advanced manufacturers, robotics companies, AI and scientific-computing organizations, portfolio companies, foundations and philanthropic institutions, family offices and strategic capital partners, technical professional societies, and individual scientists and inventors.

David Sherrer is a deep-technology founder, inventor, and operating executive whose career has focused on turning difficult physical technologies into intellectual property, funded programs, manufactured products, operating companies, and strategic acquisitions.

He founded Haleos and Nuvotronics, led the commercialization of PolyStrata® advanced RF and microsystems technology, executed DARPA-backed development through Phase I–III, built manufacturing capability, and has more than 125 issued patents worldwide.

MNEOS emerges from a lesson repeated across those experiences: technical capability does not compound automatically. It compounds only when people, evidence, tools, decisions, manufacturing knowledge, and institutional memory are intentionally connected and preserved.

If you are working on a difficult physical-world problem — or believe your experience can help build the institution itself — we would like to hear from you.