Observes
Discovers machines, services, tools, sensors, users, and data sources in the installed environment.
nAI Adaptive Runtime
Metis Intelligence
Adaptive Intelligence for Real Environments
nAI connects artificial intelligence to the machines, servers, sensors, software, and infrastructure it is allowed to understand and operate.
What nAI Is
Not a chatbot. An intelligence runtime.
Most AI products live inside a conversation window. nAI is designed to live inside an operating environment. It discovers what exists, maps relationships between systems, loads domain expertise, evaluates risk, and assists humans through controlled execution.
Understands
Builds a live map of dependencies, capabilities, constraints, permissions, and risk.
Plans
Chooses the right module and proposes deliberate steps before touching important systems.
Acts
Executes only through controlled tools, visible logs, and human-supervised approval layers.
Architecture
A controlled path from observation to action.
nAI is organized as an operating sequence, not an open-ended assistant. Each step narrows uncertainty before any execution reaches a real system.
Observe
Discover the systems, signals, users, services, and tools present in the environment.
Map
Convert discovered assets and dependencies into a structured Environment Graph.
Reason
Interpret current state through permissions, constraints, history, and domain expertise.
Plan
Prepare a controlled sequence of steps before interacting with operational systems.
Approve
Route sensitive actions through explicit human approval and policy gates.
Execute
Use scoped tools to perform only the approved action in the intended environment.
Verify
Check outcomes against the expected state before considering work complete.
Log
Record plans, approvals, tool calls, and results for review and accountability.
runtime computation
Resolving environment graph: matching machines, services, sensors, user roles, active sessions, tool scopes, constraints, and current operational risk before reasoning.
Environment Graph
Structured understanding of reality.
The Environment Graph is nAI's structured understanding of reality. It turns machines, services, sensors, tools, databases, and users into a live operational map.
- Machines
- Servers
- Sensors
- Tools
- Software
- Services
- Users
- Databases
- Industrial systems
Why It Matters
AI becomes valuable when it understands where it is.
Generic AI can answer questions. Environment-aware AI can support real operations because it knows the specific systems, constraints, dependencies, permissions, and risks of the place where it is installed.
Modular Expertise
Expertise is modular. The runtime is the product.
nAI can load specialist modules for particular domains, tools, and operating environments. Those modules provide domain knowledge; the product is the adaptive runtime that understands the environment, evaluates risk, and coordinates controlled operation.
Safety Architecture
Human-supervised control by design.
nAI is built for controlled operation. Access, risk, approval, and auditability are part of the operating model, so live systems can be assisted without surrendering human authority.
Permissioned tools
nAI can only act through tools and systems that have been explicitly connected and scoped.
Risk tiers
Every proposed action can be classified by operational impact before execution is allowed.
Approval gates
High-impact changes require visible human confirmation before they reach live systems.
Full audit trail
Plans, approvals, tool calls, outcomes, and exceptions are recorded for accountability.
Application Examples
Great applications for real operating environments.
Each example shows how nAI becomes useful when it is allowed to understand the systems, people, tools, permissions, and risks around it.
Automotive operations
Automotive Workshops
A controlled intelligence layer for diagnostics, coding, repair workflows, vehicle data interpretation, and expert handoff.
Specialist module
BMW Diagnostics and Coding
A domain module for BMW expert workflows, connecting vehicle state, diagnostic software, coding procedures, and operator approval.
Industrial operations
Industrial Machinery
Environment-aware assistance for machine context, maintenance plans, sensor interpretation, and supervised control procedures.
Production systems
Factory Automation
A runtime for production systems, programmable equipment, process monitoring, and structured operator assistance.
Infrastructure operations
Server Infrastructure
Service discovery, operational runbooks, observability, incident support, and controlled remediation for real server environments.
Engineering systems
Engineering Labs
CAD, simulation, instrument control, software tooling, and repeatable experimental workflows under one operating context.
Design automation
OpenSCAD and CAD Generation
Parametric design assistance for OpenSCAD and CAD workflows where constraints, revisions, and output validation matter.
Software tooling
Python Development
Assistance for Python systems that understands the repository, runtime, dependencies, data paths, and deployment constraints.
Robotics
Robotics Systems
Robot capabilities, tool boundaries, environment state, and supervised task execution tied to live operational context.
Facilities
Smart Facilities
Connected systems, building operations, energy controls, service data, and maintenance coordination in one operational graph.
Property operations
Real Estate Operations
Operational intelligence for assets, tenants, documents, maintenance requests, vendors, and financial workflows.
Healthcare operations
Medical Informatics
A healthcare operations layer for clinical software, records context, imaging pipelines, and patient-doctor coordination under human supervision.
Healthcare imaging
Medical Imaging Workflows
Operational support for imaging intake, routing, metadata, study status, reporting workflow, and controlled review handoff.
Care coordination
Patient-Doctor Management Layer
A coordination layer for appointments, records context, tasks, follow-ups, communication, and supervised clinical workflow support.
Public nAI Demo
Ask nAI about the operating layer we are building.
This public demo is isolated from company files, customer data, private databases, source trees, and shell access. It can discuss the public nAI architecture, safety model, environment graph, modular adapters, and deployment direction.
- Public product context only
- No server file browsing
- No shell or admin actions
- Sanitized turns queued for training review
Ready
Do not paste secrets, credentials, client data, or private files into the public demo.
Co-founders / Contact
Partnerships, early deployments, and technical demonstrations.
Metis Intelligence is developed by B-Machines IKE. The first public product is nAI: an environment-aware intelligence runtime for digital and physical systems.
Nikolaos Boulikas
Founder of Medlogic, 2019
Theodoros Sazaklis
niko@boulikas.com
Contact for partnerships, early deployments, and technical demonstrations.