Your own AI, as powerful as a company.
The AI-native operating system for autonomous business.
A person you name approves anything that can’t be undone, like sending money. Every step is recorded, and you can leave with your data, setup and agents.
Irreversible actions, including money movement, wait for named human approval. Every action is traceable, and owned data and setup stay exportable.
When there is more work to do, you should not have to hire more bosses.
One versioned definition renders more governed agents, workflows and cells, so organizational scale stops tracking human headcount.
Keep the people you have. Every time someone fixes an agent's mistake, the whole system keeps that fix.
The system captures outcomes, approvals, overrides and provenance. Only validated, customer-safe class patterns may improve the shared rendering library.
First we learn how your company really works: who decides what, and where the time goes. Then we show you the first job an agent can take on.
The audit establishes the source model for workflows, controls, data boundaries, intervention points and the destination architecture used by the Cell Factory.
Someone from our team sits with you and your staff. We follow one job from the first email to the last click, and we write down who touches it.
A structured discovery captures actors, artifacts, workflow states, control points, failure modes and measurable outcomes. It is the evidence layer for every journey.
Free consultationHuman-reviewed discoveryWe draw a map of every job in the company and mark the ones that waste the most time. Then we agree on the exact list we will build.
The team produces a workflow map, ranked value-leak register, destination cell architecture, control envelope and fixed build scope. Startup skips Phase 2 and begins with the standard product.
You know what gets builtFixed build scopeEveryone starts here. Where you go next depends on the work you want to change.One start. Three deliberately different routes to an AI-native company.
Everyone starts with the same free consultation. Where you go next depends on the work you want to change and how fast you want to change it. The sizes below are a guide, not the rule.
The three routes share one entrance and protect three different human promises: founder count, existing roles, and the running original. The route follows from the work, the constraints and the change strategy, not from headcount alone.
01
Still just the foundersFounder-run at every scale
You add agents instead of people. That is how it is built.Capacity is designed to arrive as agents rather than as hires.
You start with six agents: a Super-agent that runs the day, a Lifecycle and Commercial agent, a Curator for the company memory, a Finance agent, a Loop-closer and a Growth agent. They run recurring work while you set direction, approve consequential decisions and stay in control. When the work gets heavy, we add more agents, not more layers of administration, so the same founding team can do far more.
Phase 1 audit → standard startup cell → six core agent classes. Parameterized upgrades increase agent and workflow capacity while ownership, purpose, memory and governance remain continuous. The owner and team retain authority; the operating system supplies coordinated execution.
02
Same team, much more workSame people. Enterprise output.
Every person on your team gets agents to run.Every employee becomes a manager of agents.
After the consultation and audit, we rebuild how the work flows around the people you already have. Each person gets agents that report to them. Your sales lead still signs off the outreach. They just stop typing it.
Phase 1 + Phase 2 → workflow-specific agent teams → human approval boundaries → override capture → governed learning. Existing roles retain accountability while agent spans grow around them.
03
Prove it before you switchEvidence before migration
Nothing gets switched off until the copy does the job better.The twin takes over by outperforming the original, one workflow at a time.
Pick one job your company does, like paying bills. We build an AI version of that one job and run it next to the real one. Both do the work. You compare them. If the copy wins, it takes that job over, and we start on the next one.
Phase 1 + Phase 2 → workflow twin → isolated parallel operation → evidence comparison → progressive twin expansion. Migration stays a reversible decision until measured superiority is established.
These are examples of work the system can do. They are not customer results.Illustrative operating scenarios, not reported customer outcomes or performance claims.
An email lands from a customer who wants to change an order. The Super-agent reads it and sends the shipping part to the Lifecycle and Commercial agent. The refund goes in front of you, because money moves.
Intent classification, task routing inside the cell, and a policy-controlled approval boundary on the refund with named authority, timestamp, rationale and an immutable audit event.
The Finance agent works with connected bank activity, keeps the books and checks the numbers before saving them. The books stay on a computer the business owns. It can recommend a payment. It never moves money.
The Finance class integrates bank activity with exact-arithmetic controls. C3 keeps finance work on local inference with no cloud-model fallback, lane or subagent.
It writes three versions of Thursday's email to your customers. It shows you the kind of subject line you said yes to last time. Nothing is sent until you pick one.
Draft generation conditioned on approved prior outcomes held in the cell's operational memory. Send is gated on human approval and recorded with rationale. Rejected drafts feed the loop as well.
It writes down what happened today, so the answer is there next time. Beside each fact it puts a note. The note says where the fact came from and who checked it.
Operational memory write with provenance metadata and knowledge-quality scoring. The cell owns this memory and can export it. Only provenance-cleared class patterns are eligible to leave it.
It goes through every open item from the day. Each one is either done or handed back to a named person. It never marks something finished when it is not.
Open items are chased to completion or explicit human hand-back. Completion is recorded only against evidence, never assumed.
The Super-agent checks the Curator's notes before it answers. The Growth agent asks the Curator what you said last time. You see all of it. You can stop any of it.
Coordination across one shared operating memory and a single objective set, under one governance envelope. Cross-agent requests cross the same policy boundaries as external ones, and every hand-off is written to the same event trail as the work itself.
The agents do not change. What changes is who is in the room with them.
The core agent set is constant across tiers. Size, depth and deployment parameters change, and so does the human accountability structure around them.
The agents take the jobs you would otherwise hire for. The Growth agent drafts the outreach. The Curator keeps the notes. The Loop-closer chases what is still open. When the work doubles, we add more agents. It is still just you.
A standard startup cell coordinates six core agent classes under human ownership. Parameterized upgrades raise agent and workflow capacity while purpose, memory and governance stay continuous across the business.
Your sales lead still owns the deals and now runs a Growth agent. Whoever owns the inbox now runs the Super-agent. Each person checks the work and fixes what is wrong.
Workflow-specific agent teams under existing role accountability, with human approval boundaries, override capture and governed learning. Agent spans grow around the roles rather than replacing them.
The agents run one job inside the copy. Your real company keeps running the same job the old way. You watch both for a while, then you decide.
Workflow twin under isolated parallel operation, then evidence comparison and progressive twin expansion. Migration stays reversible until the twin measurably outperforms the original.
You say yes or no to anything you cannot take back. You fix what an agent gets wrong. You decide what gets built next. The system keeps every fix you make.
Named human approval, override capture and scope decisions stay with the client at every tier. Override rate governs how much an agent is trusted to complete on its own.
stay the only peoplestay the only humans
gets agents to runbecomes an agent manager
keeps running the whole timekeeps operating during proof
The agents do the work. You say yes or no to anything that matters, and you fix what they get wrong. The agent writes your fix down, so next time it does the job your way.
The closed loop records requests, actions, outcomes, human approvals and overrides. Override rate governs completion and trust; provenance-cleared class patterns can then enter the shared RSI library without moving customer data.
We wrote down once how a company like yours should run. Then we build your version of it. People do the listening and the design. The machine does the setting up.
A canonical versioned system definition is rendered with independent size, depth and deployment parameters. Every client receives an isolated cell with shared purpose, objectives, heartbeat, owned memory, policy boundaries and a persona gateway.
Licensed expertise is mediated at the boundary; persona and IP remain controlled while each cell owns its operational memory.
We build every client system against these three rules. They keep people in control, sensitive information in the right place and every important action accountable.
C1, C3 and C4 define the deployment contract: portable ownership, sovereign financial processing and authenticated human authority over irreversible actions.
Some jobs cannot be undone, like sending money or deleting a record. On those, the software stops and waits for an authorized person. The approval records who decided, when and why.
C4 routes irreversible actions through authenticated human approval with named authority, timestamp, rationale and an immutable audit event.
The Finance agent works with connected bank activity on a computer the business owns. Books and raw financial records are not sent to an outside AI.
C3 binds finance work to local inference with no cloud-model fallback. Raw financial records stay excluded from shared learning and external model routes.
Every important step an agent takes is written down: what it did, why, what changed and who approved it. You can stop the system, correct its work and take your data, setup and agents with you.
Requests, tool calls, model outputs, approvals, overrides, outcomes and provenance form a complete event trail. C1 keeps owned data and system export portable while licensed persona and IP remain licensed.
Alien Cortex started with a promise Andrey made to his wife, Shaila. She loves creating surface pattern designs, but not the administration, marketing, accounting and back-office work that comes with running a business. Andrey offered to build ChroMaxStudio as a company operated by AI, so Shaila could concentrate on the creative work she loves.
That first company became the beginning of a much larger journey. Andrey kept refining the agents inside his own businesses, then turned what he learned into aBOS, the Alien Business Operating System, and the AI-native company factory: a repeatable way to build companies around human strengths while AI carries much of the operational load.
The journey began with ChroMaxStudio: an AI-operated business designed around Shaila’s surface pattern work. Building the administrative, marketing, accounting and back-office support around one creative person exposed the recurring operating roles that agents could take on—and the limits of configuring each company by hand.
Andrey refined the system across his portfolio companies, evolving it into aBOS and the Cell Factory: a canonical definition that renders isolated client cells, with the AI Audit as the evidence-gathering front door. Eight U.S. provisional patent applications have been filed around the system; they have not been granted.
Andrey has spent more than 20 years building companies in technology, finance and real estate. He leads Alien Cortex and is building aBOS so business owners can save time and get more done.A multidisciplinary operator with more than two decades across technology, finance and real estate, Andrey architects aBOS, its governed agent model and the Cell Factory that renders AI-native companies.
Shaila leads operations and creative work. Her love of surface pattern design inspired ChroMaxStudio, the first business Andrey built to be run with AI help.Shaila leads operations and creative direction, is the creative force behind ChroMaxStudio, and is a named coinventor on U.S. provisional patent applications covering the aBOS system.
Julie welcomes new clients, asks what work is slowing them down and helps them start a free consultation. Julie is an AI agent, not a person.Julie is the public-facing Sales and AI Audit Intake persona. She structures initial discovery signals and routes qualified context into the human-reviewed engagement process.
Augura keeps plans, choices and follow-up work organized across Alien Cortex. Augura is an AI agent, not a person.Augura is the public-facing AI Chief of Staff persona, maintaining alignment across priorities, decisions, ownership and execution follow-through.
Julie and Augura have names and faces because clients meet them. The agents working inside aBOS do not pretend to be people. Every setup has the same six, at any size, and none of them is optional. As the work grows we add more agents inside these six jobs, never a new kind of authority.Julie and Augura are named public-facing personas. Inside aBOS, role-bound agents operate without synthetic identities. Super-agent, Lifecycle and Commercial, Knowledge and Brain (Curator), Finance, Loop-closer and Growth are the six mandatory classes in every cell, coordinating under one governance envelope, shared objectives and owner authority. Additional agents are added inside these classes as the workflow map justifies; growth never adds a new class or new authority. A background governance monitor is part of the platform, not a seventh agent.
Runs the day, hands out the work and brings the big calls to you.Orchestrates and delegates across the cell, keeps the heartbeat and escalates decisions through the governance envelope.
Handles onboarding, proposals and renewals. It drafts. A person sends.Onboarding, proposals and renewals. Outbound is draft-only and gated on human send.
Takes in files, mail and notes, keeps the company memory in order and finds things again.Curates tiered, exportable company memory with provenance. Finance data is never promoted into general memory.
Keeps the books, forecasts and watches cash on a computer the business owns. It never moves money.Local-only inference with no cloud fallback and no subagents. Recommends; never moves money.
Chases every open item until it is done or handed back to a person.Tracks open items to completion or explicit hand-back. Never marks incomplete work complete.
Finds demand and writes outreach. Nothing goes out until you say yes.Demand, positioning and outbound drafts, plus an investor pipeline where needed. Sends nothing without a human click.
We filed eight patent applications with the United States patent office. They cover how the system is built, how it stays under your control, and how it learns. None of them are granted yet.
Eight U.S. provisional patent applications cover the architecture, the controls, the compounding mechanisms and the agent interfaces.
First filed March 20, 2026. It is an application, not a granted patent.Filed 20 Mar 2026 · patent pending.
First filed June 12, 2026. It is an application, not a granted patent.Filed 12 Jun 2026 · patent pending.
First filed July 29, 2026. It is an application, not a granted patent.Filed 29 Jul 2026 · patent pending.
First filed July 30, 2026. It is an application, not a granted patent.Filed 30 Jul 2026 · patent pending.
First filed August 14, 2026. It is an application, not a granted patent.Filed 14 Aug 2026 · patent pending.
First filed August 14, 2026. It is an application, not a granted patent.Filed 14 Aug 2026 · patent pending.
First filed August 14, 2026. It is an application, not a granted patent.Filed 14 Aug 2026 · patent pending.
First filed October 3, 2026. It is an application, not a granted patent.Filed 3 Oct 2026 · patent pending.
Filed, not granted. A first filing holds your place in line at the patent office.Provisional patent applications filed. This page does not describe them as granted patents.
Short answers. Open a row for the rest.
Short answers. The deep layer carries the mechanism behind each one.
No. The owner and team keep responsibility and gain agents to run. In a one- or two-founder company, agents add capacity without adding administrative layers. The system is designed to multiply what people can accomplish—often by 10× and, for highly repeatable work, potentially up to 100×—while giving them back time for judgment, relationships and creative work.
The SMB journey holds existing role accountability constant and grows governed agent spans around it. The Startup journey adds coordinated digital capacity while human owners retain strategy, approval authority and accountability.
They stay on the computer that runs the Finance agent. The agent works with the connected bank, but its books and raw records are not sent to an outside AI.
The Finance class integrates bank activity inside the client cell. C3 keeps finance requests on local inference with no cloud-model fallback.
You fix it, the same way you would fix a new person's work. The agent keeps your fix and does it your way next time. Anything you cannot take back waits for a person to say yes first.
The closed loop captures each override with the original output, the edit and the rationale. Irreversible actions stop for authenticated human approval, preserving owner control while the system learns.
Yes. Your data, your setup and your agents leave with you. Portability is built into the operating model on purpose, so you stay in control and never get trapped by the platform.
C1 keeps export of owned data, configuration and client models outside any payment condition. Licensed persona and IP remain licensed.
No. If you can tell a new person what good work looks like, you can run an agent. That is the whole skill.
The operating skill is specification and review, not configuration. The audit and the build stay with our team; the client's ongoing role is approval and correction.
The consultation is free for everyone. We do not put prices on this page because we do not know what you need until we have looked. After the consultation you get a fixed list and a real number.
Phase 1 is free for every client. Scope and price follow the Phase 2 workflow map and the fixed build scope it produces.
Someone from our team sits down with you and works out how your company runs today. That part is free, and it ends with a list of what an agent could take on first. See what the free consultation covers.
Phase 1 discovery produces the evidence layer: actors, artifacts, workflow states, control points and measurable outcomes. See the audit structure.
Book a free consultation. We’ll learn how the work happens and show you where AI can help first.Begin with AI Audit intake so we can identify actors, artifacts, control points, constraints and the right client-cell trajectory.
Two founders or two thousand staff, you start the same way. Someone sits down with you and works out how your company runs today.
Startup, SMB or Enterprise, every route starts by mapping the business you already have.
Book a free consultationStart the AI Audit The consultation costs nothing. We do not list prices, because we do not know what you need until we have looked. No prices on this page. Scope and price follow the audit evidence.