COMING SOON!
The Future of Intelligence Systems
“queOS™ — Intelligence Built Into the Core.“
Quantumeron Labs’ patent-pending queOS™ is a hybrid-accelerated, Neuromimetic Adaptive Operating Intelligence System (OIS) — intelligence built into the core.
queOS™ treats cognition, learning, memory, communication, and execution as an integrated nervous system, governed through a policy-first control loop (govern → decide → act → verify) with persistent memory and verifiable execution.
Unlike model-first AI, queOS™ does not depend on a single frozen model. It governs and coordinates replaceable cognition execution domains and specialized processes that operate in context, adapt over time, and respect policy and data-ownership boundaries.
queOS™ runs on today’s classical infrastructure and may selectively leverage heterogeneous accelerators where they meaningfully improve performance — without changing governance, accountability, or verification behavior.
Self-Evolving AI
Systems that get smarter in context—fast. queOS™ improves through governed learning loops and real outcomes, not just slow retraining cycles.
Built-In Privacy
Privacy isn’t a feature—it’s built in. queOS™ enforces policy-aware learning and permissioned attribution so data stays controlled, minimized, and auditable.
Anywhere, Any Scale
Edge to cloud to future accelerators—same core behavior. queOS™ runs wherever the work happens, stays resilient offline, and produces verifiable execution records.
Quantumeron Intelligence Systems Announces USPTO Filing of queOS™, Anticipating Risks Later Highlighted in MIT’s Generative AI Discussions
Quantumeron Intelligence Systems announces the US Patent & Trademark Office filing (June 6, 2025) of queOS™, a neuromimetic AI operating system that reimagines how AI learns and adapts. Unlike large language models (LLMs) that depend on vast copyrighted datasets and costly retraining, queOS™ enables lean, real-time, role-adaptive learning within a privacy-first, modular architecture—reducing copyright and data-provenance exposure.
Importantly, this direction did not require months of lab studies or a large research cohort. Inventor and Founder, Cecilio Lorenzo, relied on first principles and common sense: if AI is trained on massive, mixed-provenance content, it will inherit copyright and compliance risks; if it must be retrained endlessly, it will remain slow and expensive to deploy. From those straightforward premises, quetOS™ was engineered as an operating system—not another LLM—to learn in context and adapt on the fly.
Just one month after queOS’s USPTO filing, MIT’s July 2025 generative-AI studies and reports publicly underscored the same industry risks around copyright, opacity, and governance. While entirely independent, those findings echoed the concerns Quantumeron had already addressed in queOS’s foundational design.
“queOS™ isn’t another model—it’s an Adaptive Intelligence OS,” said Lorenzo. “We built it to be lighter, safer, and faster to deploy, so innovators can move from MVP to pilot without inheriting legal and operational drag.”
Quantumeron’s mission is clear: deliver AI that is powerful and responsible. queOS™ represents a decisive step toward that future.
queOS™ Comparison
Unlike static large language models (LLMs) that rely on frozen knowledge and centralized processing, queOS™ operates as a dynamic nervous system—processing real-time sensory input, adapting to new threats, and making ethically constrained decisions as events unfold. No more batch-mode thinking. No more latent biases from stale training data. Just fluid, context-aware intelligence that evolves with its environment.
queOS™
queOS™ operates as a dynamic nervous system, with:
- Integrated layers for cognition, learning, memory, communication, execution, and governance
- Real-time sensor and event integration
- Localized learning loops that adapt on the edge
- Ethical guardrails and policy enforcement at runtime
- A self-tuning architecture that can reconfigure under strict constraints as conditions change
- Designed to minimize hallucinations by grounding cognition in shared memory, real tools, and explicit policies, with an architecture built to constrain and audit behavior and to surface where answers come from.
Traditional LLMs
Large Language Models (LLMs), such as ChatGPT or DeepSeek, typically rely on:
- Frozen knowledge cutoffs
- LLM apps often hallucinate — fluent answers with no grounding.
- Centralized cloud training on massive mixed-provenance datasets
- Post-hoc alignment and safety patching
- Static parameter weights that require expensive retraining to reflect new reality
Why the World Needs an Intelligence OS
Over the last few years, we’ve gone through some clear phases in AI:
More recently, early “AI OS” and orchestration layers — mostly wrappers on top of models, wiring tools and workflows together.
First, an explosion of LLM apps — single chatbots and point tools, all pretty isolated.
Then “one giant library” models exposed via APIs — like a brain in a jar, sitting off to the side.
Most of what we see today is still:
- very App-centric,
- stitched together with brittle, daisy-chained workflows(using Zapier, n8n, etc),
- missing any real shared memory or world model,
- and not really learning as a system — things only improve when someone retrains a model or edits prompts.
The New Era of OS
Operating System Evolution: From Hardware Control to Adaptive Intelligence
Timeline:
PC era – DOS → Windows
- Started with text-based DOS, then shifted to graphical operating systems like Windows that made PCs usable for everyone.
Mobile era – Feature phones / PDAs → iOS & Android
- Early mobile/PCS devices ran simple, limited firmware.
- True mobile computing arrived with smartphone OSs (iOS, Android) running rich apps on the move.
Cloud / Web era – Desktop-centric → Browser (Chrome) as primary runtime
- Apps moved from local installs to browser- and cloud-first experiences, with Chrome and similar browsers acting like a lightweight “web OS.”
Artificial Intelligence era – AI models → queOS (Adaptive Intelligence)
- Today, we have powerful AI models but no true AI Operating System.
- queOS is designed as that next layer: an Adaptive Intelligence OS that manages cognition, learning, memory, communication, and execution across systems.
Witness the AI Neuromimetic Revolution
queOS™ takes inspiration from the human nervous system’s speed and adaptability in silicon—moving a step closer to more brain-like cognition in machines.
Healthcare Workforce Readiness, Automated
Our first pilots focus on healthcare workforce readiness and compliance. queOS™ orchestrates LMS, VR training, on-the-job assessments, credential checks, and audit-ready evidence into one adaptive workflow—so staff stay competent, credentialed, and ready for inspections without adding more admin burden.
Lifesaving Drones with Machine-Speed Reflexes
AI that reacts with machine-speed reflexes—navigating disasters, dodging debris, and coordinating with other systems in real time. queOS™ can unify sensing, decision-making, and execution across a swarm, instead of relying on one central brain.
Security-First Executive Assistant
Your boardroom’s secret weapon—on-device and compartmentalized AI that handles deals, drafts, and redactions with a minimal attack surface. Sensitive discussions can be analyzed, summarized, and actioned without defaulting to persistent, centralized storage.
Hospital AI That Learns from Workflows, Not Raw Records
Train diagnostic and workflow assistants using patterns, signals, and role-based context—while PHI stays local. queOS™ is designed to support HIPAA-conscious architectures that minimize data exposure, reduce centralization risk, and keep clinicians in control.
Be the First to Experience AI That Thinks Like You
Get exclusive updates on queOS™—where machines learn, adapt, and react with biological-style precision, not just statistical prediction.
Where we’ll start:
- Early pilots in healthcare workforce readiness and compliance, built on top of queOS™
- Future experiences like Nexus, a multi-agent command center that overlays your existing apps and workflows in healthcare
- QORA, an always-on personal assistant that orchestrates home and work across the tools you already use—powered by queOS™
Why Join?
Early access previews of a Neuromimetic AI Operating System
Priority invites to live demos (healthcare, defense, smart cities, and more)
Behind-the-scenes looks at our first pilots and learnings from real-world deployments
Curated intelligence on AI that evolves—not just computes
If you’d like to connect with us about queOS™ or our upcoming pilots:
Press & Newsroom
For interviews, speaking opportunities, or press coverage:
📧 press@quantumeron.com
Investors
For investment conversations and our latest materials:
📧 investors@quantumeron.com
Pilots & Partnerships
For potential pilot sites, enterprise collaborations, or technology partnerships:
📧 partners@quantumeron.com