An identity
Who it is, who it reports to, which systems it may touch, and who answers when something goes wrong. LMCC keeps all of that in one identity; permissions, tasks, and workspaces hang off it and survive the next conversation.
AI Workforce Infrastructure
Turn AI into
Turn AI into
Your Workforce
LMCC is infrastructure for digital employees: a registered identity for AI, a readable map of the business, and a workplace where it can act. Once on the job, it makes its own calls and answers for the results.
Background: ESO/VMC Survey (M.-R. Cioni et al.), VISTA telescope, CC BY 4.0
Origin
The rigor of astronomy,
applied to what AI does next
LMCC comes from the Large Magellanic Cloud, one of the Milky Way's neighboring galaxies, about 160,000 light-years away. It is also one of the galaxies we can observe and study directly.
In the past, ships, telescopes, and space probes kept extending the boundary of what we know.
Today, AI is stepping out of the chat window into software, data, business systems, and real computing environments.
We keep the courage to explore the unknown, and the patience to understand the world, so that AI leaves the chat and enters the organization.
Core Pillars
To stay on the job,
AI needs three things
Close the chat window and a chatbot's work is over. To stay in an organization, AI needs an identity, a grasp of the business, and a way to act on it.
A map of the business
A pile of APIs is not enough. It has to know how inventory, orders, and suppliers relate, what state they are in, what is allowed, and what happens next. LMCC's Operational Ontology turns that into a structure it can read and act on.
A place to work
Understanding has to end in action. LMCC wires AI's decisions into real computers, servers, and business systems: calling APIs, operating software, reading the results, and choosing the next step.
Ontology · Business Integration
Turn the business world
into a structure AI can read
Business systems, tickets, and approval flows were built around people. Before AI can take part, the objects, relationships, states, and rules inside them have to be spelled out.
One world model
LMCC turns these into an Operational Ontology, one model that can be read and acted on.
Maintained by the business team
Business teams define and change the objects, rules, and actions. People and digital employees read the same facts and follow the same rules.
Business World
Objects · Relations · State · Events
Operational Ontology
Rules · Actions · Decisions
Digital Employee
Understand · Decide · Act
The data and the rules have to be organized into an ontology first — that step cannot be skipped. LMCC's ontology tools help companies get through it faster.
The Platform
Three layers,
from the machine up to the job
The bottom layer handles machines and execution, the middle layer runs the digital employees themselves, and the top layer carries industry capabilities. Each digital employee has its own permissions and workspace, tasks are dispatched centrally, and every operation is recorded.
Business Layer
Domain Runtime Assembled by industryBuilds job roles for each industry. Each business domain plugs in with its own job skills and knowledge: financial risk control, smart manufacturing, retail operations, enterprise R&D, and customer service all connect the same way.
Finance · Manufacturing · Retail · R&D · Customer Service
Intelligence Layer
AI Runtime From models to employeesTurns a series of model calls into an employee who stays on the job. This layer keeps its memory and goals, arranges its work with colleagues, and handles onboarding, reassignment, and offboarding.
Identity · Memory · Goals · Governed
Foundation Layer
Platform Runtime Runs on the machines you haveThe execution client is a lightweight program that runs on the office PCs, servers, and even shop-floor industrial machines you already own, on Windows, macOS, and Linux. Through it, digital employees operate software, handle files, and sign in to business systems; legacy systems without an API are driven through their screens. Your existing IT stays as it is.
Office PCs · Servers · Industrial machines · No IT overhaul
Tasks out, reports back
Headquarters Brain
Where digital employees decide, take assignments, and are managed
Task Assignment Unified
Decisions Central Governance
Assign tasks ·
Execute on site
Execute on site
Report progress ·
Take over anytime
Take over anytime
Workplace Hands
Execution clients on staff PCs, servers, and devices
Windows macOS Linux Legacy Industrial
People can connect from any device to check progress or take over directly.
Governance
Permissions, approvals,
audit, and budgets
Manage digital employees the way you manage staff. Permissions, approvals, audit, and budgets are built into the platform.
A human signs off on what matters
High-risk actions run as a dry run first, with no real effect; they execute only after an owner signs off. Permissions reach down to specific objects and operations.
Every step, on the record
Every judgment and action leaves an audit record, so work can be reviewed, responsibility is clear, and internal audit and compliance reviews have what they need.
Boundaries and budgets, set in advance
You can set resource and spending limits for each digital employee. Repeated errors pause the work automatically and notify the owner.
Open model choice
No lock-in to a single model vendor. Pick a model per task, switch any time, and keep using the models you already pay for.
Evolution
From Agent to AI Workforce
What separates the three stages: an identity, a memory, and the ability to work with others.
Agent
Uses tools and gets one specific job done. No lasting identity, no memory of last time.
Digital Employee
Identity, permissions, memory, and a workspace. It holds one role, makes its own decisions, and owns the results. What it learns is written down for others to use.
AI Workforce
Several digital employees and people divide the work, hand it on, and check each other, forming one team of humans and AI.
From the nebula in our name, we travel with AI and explore outward.
- ExplorationOut of the chat window
- WorldReads the business rules
- IntelligenceRemembers, keeps working
- Digital EmployeeHolds a post, owns the result
- AI WorkforcePeople and AI, one organization
How a digital employee
takes its place in the organization
It has a role, colleagues, someone it reports to, and decisions that are not its to make.
Personal Assistant
Digital Employee
A personal assistant follows one person. A digital employee belongs to the organization, with a position, a workspace, and long-term memory; the role stays when people move on.
A Tool on Call
A Working Partner
A tool waits to be called. A working partner moves tasks forward on its own, hands information to the next person, and picks up work passed to it.
Workplace Integration
Digital employees join your team directly
No new system for the team to learn. Digital employees sign in with the accounts you already have and take work in the chat tools you already use.
One identity, formally onboarded
Connects to your existing accounts and authentication. Permissions, approvals, and audit follow the same standards as for human colleagues, and onboarding, reassignment, and offboarding go through the usual process.
Single sign-on (SSO) Access &
Approval Audit Records
Present where work happens
In Feishu (Lark), WeCom, and DingTalk you can @ it, add it to a group, assign it a task, and read its progress reports.
Feishu / Lark WeCom DingTalk
The Work Loop
How AI works
Answering a question is not the end of the work: the result of each action becomes the basis for the next judgment.
Understand
Read the objects, the state, and the rules
Decide
Judge against the goal and the limits
Act
Call the tools and the business systems
Observe
Look at what came back
Evaluate
See whether the goal was met, then go again
If the goal isn't met, another round begins; if the goal changes, so does the course. Every round is recorded, and a person can step in, correct, or stop at any point.
Build the AI Workforce.
From one digital employee to a whole AI team.
Maggevir.AI