Data
Fragmented, inconsistent, and stripped of business meaning.
Proairos
Prohairesis, reimagined for the age of AI.
The outcome operating system for AI-resilient traditional enterprises
Prohairesis — the capacity for deliberate, responsible choice.
The operating loop
One loop, closed end to end.
Not a copilot, not a chatbot over your documents, not another agent framework. The loop that connects an objective to a verified result.
- Set the business objective
- Understand current state
- Select the best action
- Execute within guardrails
- Verify the outcome
- Learn and repeat
Every pass through the loop leaves evidence behind.
The bottleneck
AI makes individual work faster. The slowest constraint still decides the outcome.
Amdahl’s Law is unforgiving. If individual work is 20% of a workflow, even perfect acceleration buys no more than a quarter — the rest is human-speed coordination, validation, and execution.
| AI acceleration of individual work | Maximum total improvement |
|---|---|
| 2× faster | 1.11× |
| 10× faster | 1.22× |
| Effectively instant | 1.25× |
Context
Scattered across applications, documents, messages, and people’s heads.
Decision
Authority stays tied to hierarchies, committees, and approval chains.
Validation
Every AI-generated output still waits on a human reviewer.
Execution
AI recommends; employees still key it into the real systems.
Organization
Departments were designed for human labour, not for humans plus autonomous software.
Measurement
Tasks completed get counted. Margin, utilisation, and risk do not.
The enterprise AI problem is no longer generating an answer. It is turning that answer into a safe, coordinated, measurable business outcome.
A precedent
Electricity transformed factories only after factories were redesigned around it.
Act one
The steam factory
One central engine drove everything through shafts and belts, so machines had to sit where the power reached — not where the work flowed.
Act two
The electrical retrofit
Factories swapped the engine for one large electric motor and changed nothing else. The power source changed; the system around it did not.
Act three
The unit-drive factory
The gains arrived only when every machine got its own motor and the floor was rebuilt around the work — technology, architecture, and organisation changing together.
The same story, told again
| Electrical revolution | Enterprise AI |
|---|---|
| Central steam engine | Centralised human coordination |
| Shafts and belts | Departments, handoffs, and approval chains |
| One large electric motor | Copilots bolted onto existing applications |
| Circuit breakers | Permissions, policies, and risk limits |
| The electricity meter | Outcome measurement |
Proairos is not another electric motor. It is the grid, the control system, and the outcome meter for the AI-native enterprise.
From first principles
An enterprise is a system that turns its current state into a desired outcome.
Strip away the org chart and every business reduces to five things.
State
What is true right now, across every system that matters.
Outcome
What should measurably improve, and by how much.
Actions
What the enterprise is actually able to change.
Constraints
What must never be violated, whatever the upside.
Evidence
Whether the action moved the outcome — and by how much.
choose action A
to maximise outcome O
given current state S
subject to constraints C
and authorised power P
to maximise outcome O
given current state S
subject to constraints C
and authorised power P
Processes are not the objective
Traditional software asks what the next step in the process is. Proairos asks what the best authorised action is right now, given the state, the outcome, and the constraints.
A process can complete perfectly while losing money or optimising one department at the company’s expense. Processes are useful as controls. They are not the point.
The outcome contract
Every deployment starts by writing down what success is, and who owns it.
Proairos is accountable for improving a business outcome — not merely for automating the process around it.
Platform
Proairos compiles the enterprise into a model AI can understand and safely operate.
Probabilistic planning, surrounded by deterministic data, authority, policy, and evidence controls.
Raw enterprise data
Databases, SaaS applications, documents, event streams, and devices.
Enterprise Data Compiler
Schema discovery, entity resolution, semantic mapping, temporal alignment, lineage, and permissions.
Operational and organisational model
What the business is made of — and who, human or machine, is allowed to change it.
Outcome planning and decisions
Enumerate actions, simulate effects, compare alternatives, estimate confidence.
Policy, identity, risk, approval
Least privilege, financial limits, separation of duties, approval thresholds.
Enterprise action layer
Governed execution into digital systems and physical operations.
Outcome ledger
Every material decision, from expected result to attributable outcome.
Evaluation and learning
What worked, when, and with what confidence.
Enterprise Data Compiler
Turns fragmented, poorly documented, temporally inconsistent data into a semantic, time-aware, permission-aware model. This is data engineering, not prompt engineering.
Operational and organisational graph
Models how the business operates and how authority operates — so AI understands not only what can be done, but what it is authorised to do.
Outcome engine
Identifies actions, simulates effects, compares alternatives, estimates confidence, executes within delegated authority, and escalates what is consequential or uncertain.
Outcome ledger
Records state, expectation, rationale, constraints, action, and actual result for every material decision — a durable record of what works.
In practice
From advertiser commitment to verified physical delivery.
A distributed out-of-home media network: raise verified campaign-delivery margin without giving up reach, brand safety, or contractual commitments.
Today
A handoff at every step
- Sales takes the brief; a planner builds the campaign by hand
- Operations checks inventory — and sends it back when it is short
- Creative waits in a separate approval queue
- Device failures become tickets someone has to chase
- Account teams negotiate the shortfall after the fact
- Finance reconciles playback records to work out what is billable
Every arrow is a queue and a point of context loss.
With Proairos
One loop, with evidence at every step
- Sales enters the objective, audience, budget, and constraints
- Proairos compiles live inventory, device health, pricing, and audience
- Feasible portfolios are generated, priced, and reserved on the spot
- Creative is checked against machine-readable policy
- Playback evidence returns continuously; shortfalls trigger recovery
- Low-risk fixes execute within policy; material changes go to the owner
Measured on verified delivery, asset utilisation, recovery cost, campaign margin, and billing speed — not on the number of workflows completed.
Where Proairos sits
Others automate workflows. Proairos compiles the enterprise and closes the outcome loop.
| Category | Primary strength | What is still missing |
|---|---|---|
| Business intelligence | Explains past activity | Decision and execution |
| Data warehouses | Consolidate records | Operational meaning and authority |
| Copilots | Assist individual employees | End-to-end outcome coordination |
| Process automation | Repeats known actions | Contextual planning and adaptation |
| Agent frameworks | Supply building blocks | Enterprise semantics and governance |
The hard part is not the model. It is the compiled enterprise, the encoded authority to act, and the evidence of what works.
Who we build for
Businesses AI will improve — not replace.
Enterprises whose value is grounded in physical assets, locations, infrastructure, and regulated operations. Their coordination layer is where AI pays.
Sectors
Where the model fits
Signals
What a good fit looks like
How we engage
Earn autonomy one verified step at a time.
Proairos starts by watching, and advances only as its decisions prove out against yours.
Observe
Proairos runs alongside current operations and builds the model.
Explain
It accounts for what is happening and why, in business terms.
Recommend
It proposes actions your team compares against their own.
Execute with approval
Actions run once a human signs off.
Bounded autonomy
Routine actions run inside explicit, auditable limits.
Every engagement is scoped the same way
One result, and agreement in advance on how it will be judged.
Company
Built by a data engineer who has run internet-scale systems and real business operations.
Leto Bao
Founder
Previously
BaiduXiaohongshuByteDance
Founder–market fit
Leto Bao worked as a data engineer at Baidu, Xiaohongshu, and ByteDance, where operational decisions depend on complex, continuously changing data. That work is the direct antecedent of the Enterprise Data Compiler.
He is also a business operator, which is where the rest of the thesis comes from: the gap between a dashboard metric and a financial outcome. Proairos is built to close it rather than describe it.
The electrical revolution distributed power across the physical enterprise. Proairos distributes governed intelligence across the organisational enterprise — and measures whether it creates real economic value.
Contact
Tell us the outcome you need to move.
If you operate distributed physical assets through digital channels, tell us which number matters most this year.
Entity
Proairos Pte. Ltd.
Office
60 Paya Lebar Road#06-28 Paya Lebar Square
Singapore 409051