An AI-native operating system for solo founders and small businesses.
Model chats don't add up to a company. AI OfficeOS is the layer that holds the state, keeps the evidence, and leaves the owner in charge.
- Stage
- Private Alpha
- Program version
- V2.2 — current forward version
- Construction
- Pending owner authorization
- Funding
- Bootstrapped · pre-revenue
§ Why
The tools multiply. The company doesn't accumulate.
A solo owner already runs on several AI models and a dozen tools. Each one is capable inside its own window and forgetful outside it.
What builds up is transcripts. There is no single record of what the business decided or why. Evidence stays wherever it was produced. Nobody can say whether last quarter's automation actually made money. When a session closes, a subscription lapses, or a provider changes its rules, the working context leaves with it.
A model can do the work. It cannot be the company. AI OfficeOS is the layer in between.
§ What it adds
Eight things a chat window doesn't give you.
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Coordinated specialized workers
Different models take different work under one set of interfaces and one acceptance path — instead of four assistants with four opinions and no referee.
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Persistent company state
Facts, decisions and records belong to the company. A provider's session memory is scratch space, and throwing it away costs nothing.
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Evidence-backed decisions
A decision carries the evidence it was made on. Model output is a proposal until something admits it.
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Owner authority
Purpose, spending, credentials, anything irreversible, and final acceptance stay with the owner. Nothing in the system can widen its own permissions.
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Recovery and handoff
Work is packaged so another worker — or a person — can pick it up after an interruption without rebuilding context out of old chat history.
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Business outcome feedback
Results are counted in orders, revenue, cost and contribution. Not in tasks closed, tokens spent, or dashboards rendered.
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Cost awareness
Every workload has a budget and a running cost. Expensive capability goes to hard problems; routine work goes to the cheapest option that's actually good enough.
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Continuous improvement
Corrections become reusable cases and regression tests, so the same mistake gets rarer. Learning never widens what the system may decide alone.
§ Status
Where this actually stands.
This project reports its own status in the same typed form it uses internally. Nothing in this table is aspirational.
| Area | State | Detail |
|---|---|---|
| Program version | V2.2 current | V2.2 is the current forward version. It does not replace or reopen the frozen V2.0 specification. |
| Architecture & governance | Accepted | Versioned design, upgrade transition and independent review completed across multiple rounds. |
| Construction authority | Pending owner | Formal build authorization is a separate owner gate. It has not been granted. |
| First business scope | Not selected | Reserved for an owner decision. Not announced. |
| Product | Not built | No production system. No users, no customers, no deployment. |
| Revenue | Pre-revenue | Bootstrapped by the founder. No outside funding. |
§ Validation
One narrow business, run end to end.
The first production validation takes a single narrow, real business workload and runs the whole loop — rather than demonstrating features in isolation.
- Market and channel research
- Multilingual content preparation
- Lead intake
- Scheduling
- Resource coordination
- Operational evidence
- Cost and profitability review
- Structured owner decisions
The exact business scope and offer are an owner decision and are not being announced. Naming the vertical early would let one implementation harden into the architecture before anything has been proven — which is the failure this design exists to prevent.
§ Execution layer
Open-weight workers for the bulk of the work.
V2.2 is evaluating a long-running open-weight multimodal worker in the 27-billion-parameter class as a persistent, low-cost execution layer for:
- Repository analysis
- Test-failure triage
- Research preprocessing
- Documentation consistency
- Structured handoffs
- Sustained engineering and operations workloads
This is an evaluation. Nothing has been selected, deployed, or put into production.
Premium frontier models stay available and keep the difficult work. The aim is to stop paying frontier prices for bulk mechanical work — not to trade capability away for a cheaper bill.
§ Shape
Authority goes down. Evidence comes back.
§ Method
How it's being built.
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Broad architecture, thin slices
The long-term design is deliberately wide. What gets built first is deliberately narrow. A thin first version is not allowed to wall off what comes after it.
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Measured against business outcome
A capability earns its place by improving something real — revenue, cost, recovery time, or the owner's attention. Existing is not an achievement.
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Providers are replaceable
Every dependency carries an export path, a fallback and an exit. Capability is leased, never married.
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No single AI provider becomes the company
If one vendor's price change or policy change could take the business down, that's a design defect — not a cost of doing business.
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Self-hosting is not self-authorizing
Running your own models buys independence. It does not give the system permission to start deciding things for you.
§ Founder
Daren Yang
Los Angeles, California. AI OfficeOS is a solo, self-funded project.
A contact address will be published alongside the project's permanent domain.