For your team and clients
The AI governance & workspace.
Your team and their AI agents work the same projects, under one set of rules, and what each person may reach follows the work they do. An AI agent reaches only what the person it acts for allows.
Built by TEKIMAX, and used on the client software we deliver.
Your policies are validated.
The playbook is what this project has to satisfy. It is checked before the work starts, so a rule nothing could ever satisfy is caught then rather than found at the end, and it exports in the format your own tools read.
A person defines the work.
The expert sets the scope and the AI agent follows it, never its own. Its reach is that person's, no wider than the task, and every action records both.
Connects to the tools the work already lives in
Promoting a model is one decision, made once in Studio. Every call after it, from a terminal or from an agent, is counted against it.
An agent runs on somebody’s authority and never reaches further than they do. Both names go on the record beside the work.
An AI agent past its limits is refused on the call. What cannot be undone waits for a person, with the reason beside it.
How it works
From a project to a handoff
Everything is scoped to a project, and these four steps run in order on every one of them. Each says who is acting, and every check and approval is matched against your rules as it happens.
It starts with a project
The playbook it is measured against, the people on it, and what their AI agents may reach are all decided here. A rule nothing could satisfy is rejected before the work starts.
You promote what may be used
Models, skills and packages are approved for the organization before anything can call them. Anything not promoted is refused rather than quietly allowed.
An AI agent is assigned to a person
That person is on the project, and the agent reaches no further than they do. Anything permanent still waits for a named human, with the reason kept beside the work.
The client takes it over
They have been working in their portal all along. At handoff the project organization becomes theirs, the bill moves with it, and your access ends because the rows are no longer yours.
The controls
AI handles high-volume tasks within your guardrails
AI at work should let people get more done. That holds only while somebody answers for every action an AI agent takes, and every model it calls comes off a list you keep.
- CollaborationDual attribution
Every action records the AI agent that ran it and the human it serves, on one line. Credit and accountability are settled as it happens, never inferred later.
GovernanceNo exceptionsDeploying and publishing wait for a named human on your side. Reversible work never waits, and the decision and its reason are kept beside the action.
AugmentationMore doneAn AI agent can keep more work in flight than one person's capacity, and each piece stays tied to a single human owner.
ALOS brings the work together: humans, agents and workflows in one workspace.
humans
agents
Everything reversible in this run already finished. This is the only step that waited.
workflows
Integrations
Works with the tools your team already uses
How the governing happens
Three parts, in the order they act
Who it acts for
Nothing can be held to a rule until there is someone to hold it to. This is where an AI agent gets a person, and a reach that is no wider than theirs.
Anything not on the list has to be asked for. It is checked when it is used, not promised in a policy.
What it is held to
The playbook says what this piece of work has to satisfy, and it is fixed when the project adopts it. The allowed lists say which models may run and which packages may ship.
An AI agent past its limits is refused on the call. What cannot be undone waits for a person, with the reason beside it.
What gets turned back
Limits are worth having only if something turns work back. This is the part that does it while the work is happening, and hands what it stops to an approver by name.
What you can point at
Every AI agent has an owner
Every AI agent gets a login of its own and belongs to somebody. It acts for them, never as them, and it can never do more than they can.
- Never a shared key, never a borrowed login, and it never sees a password
- It can only do what its owner may do, and what they approved it to do
- Take somebody off a project and their AI agent loses it too
Anything irreversible waits
Each step's kind is decided in advance, from a written list. Reversible work runs. Work that cannot be undone stops and goes to a named human.
- Deploying, publishing and sending to a client all wait
- The approval sits beside the work it approved
- A refusal is recorded as carefully as a yes
Everything reversible in this run already finished. This is the only step that waited.
One record, written as the work happens
The record is the product, not a report generated afterward. It is written as the work happens and sealed as it lands.
- Who changed what, when, and under whose authority
- A later edit to the record would show
- Your tasks, reports and connected tools all read the same record
Every line is assigned to whoever acted, and names the human whose authority they used.
Where to start
One platform, two ways in
The mechanism is the same on both sides. What changes is the work it is pointed at, and who signs for it.
StudioSoftware companies, and the forward-deployed engineers and contractors who deliver for them.Agent runsAgent securityApproved modelsScans and approved packagesCommand lineOpen
Customer portalThe client paying for the work, and the people they answer to.Client portalApprovalsAudit trail and evidenceOpen
From the newsroom
What we published lately
Security · standardsThe OWASP LLM Top 10 for 2026.For the first time the list is checked against real incidents, not only expert opinion. The order moved, one risk was renamed, and the guidance underneath is the thing we build for: assume the model gets fooled, and make sure nothing important breaks when it does.Read it
WorkforceBuilding the next generation of AI talent.TEKIMAX runs a Department of Labor Registered Apprenticeship: paid career pathways in AI and cybersecurity, and no four-year degree required.Read it
Governance · standardsThe NIST AI RMF, at any size.The common language of AI risk in the United States, and the reason your customers are suddenly asking about it. Why a ten-person company needs it as much as a global enterprise, and what it will not do for you.Read it
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Say what you are building and who asks you about it. A person reads it and writes back. No demo you have to sit through before anybody answers a question.
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The AI governance & workspace.
Tell us what you are building and who asks you about it. We will show you the record it would leave behind.