TEKIMAXALOS

AI governanceHuman augmentation

Govern the build. Augment the human.

Your own engineers, your contractors and their AI agents, in one place. Every person gets a role. Every AI agent has an owner and reaches only what that owner allowed. Every action names both, on the record, as it happens. What your people learn from it stays theirs.

Built by TEKIMAX, and used on the client software we deliver.

Rules that compile

Your project's playbook is checked by the platform before anyone works to it, and a rule nothing could ever satisfy is rejected up front. It exports as OSCAL, so your own tools can read it.

A person directs it, and bounds it

An engineer says what needs doing and the AI agent works beside them, reaching only what that engineer is allowed to reach. Every action names both of them, written as it happens.

Connects to the tools the work already lives in

  • GitHub
  • GitLab
  • Jira
  • Linear
  • Asana
  • Notion
  • Slack
  • Figma
  • Miro
  • Google Docs
  • Airtable
The playbook's governance canvas, phase one: five control cards laid out on a dark isometric board - shadow-AI blocking, data provenance, ownership on record, app binding, and the approved-use inventory, which is selected - with the inventory's control panel open on the right listing the endpoints it is wired to and the gates it waits on

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 that somebody signs for it first. Both names go on the record beside the work.

Some things cannot be recalled. Those wait for a person, and their decision is written beside the work with the reason they gave. Everything reversible has already run.

The controls

The AI agent takes the volume, inside your guardrails.

AI at work should let people get more done, not replace them. That holds only while every action an AI agent takes is one somebody answers for, and every piece of data it touches is handled on terms you set.

  • CollaborationTwo names

    on every action: the AI agent that ran it and the human it works for. Both sit on the same record, so credit and responsibility are never reconstructed after the fact.

  • GovernanceNo exceptions

    Deploying, merging, and publishing stop before they run and wait for a named human on your side. Reversible work never waits. What was decided, and why, is kept beside the action.

  • AugmentationMore done

    by the same people, because the AI agent takes the volume and the human keeps the judgment. Nobody is replaced to pay for it.

ALOS brings the work together: humans, agents and workflows in one workspace.

humans

agents

Some things cannot be recalled. Those wait for a person, and their decision is written beside the work with the reason they gave. Everything reversible has already run.

workflows

Integrations

Works with the tools your team already uses

  • GitHub
  • GitLab
  • Jira
  • Linear
  • Asana
  • Notion
  • Slack
  • Figma
  • Miro
  • Google Docs
  • Airtable
  • Linear
  • Asana
  • Notion
  • Slack
  • Figma
  • Miro
  • Google Docs
  • Airtable
  • GitHub
  • GitLab
  • Jira

What you can hand someone

Three artifacts, and the rest is detail

  • The record

    One sealed history of the work, with the AI agent and the human named on every action. Hand it to a client or an auditor without preparing anything first. It exports in a published standard, at no charge.

  • Anything not on the list has to be asked for. It is checked when it is used, not promised in a policy.

    The parts list

    Every package the software is built from, at the version that shipped, with its license beside it. The thing a client's security team asks for, produced by the build rather than assembled for the meeting.

  • Some things cannot be recalled. Those wait for a person, and their decision is written beside the work with the reason they gave. Everything reversible has already run.

    The approval

    The moment a named person said yes, kept beside the thing they approved and the reason they gave. Not a timestamp in a separate system that somebody has to correlate later.

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
Agent runs

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, merging, publishing, sending to a customer, and changing how data is stored all wait
  • The approval sits beside the work it approved
  • A refusal is recorded as carefully as a yes
Approvals

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
Audit trail and evidence

Every line is assigned to whoever acted, and names the human whose authority they used.

Earn while you learn

The apprentice's portal on its Lessons tab: one folder per course, one course opened to show two lessons with their hours, parts and quick-check counts, each with a Start button
A lesson open on the apprentice's portal: the contents rail down the left, a check-yourself question in the middle with a box to record the answer from memory, and the page count at the top

Train the people who run your AI agents

AI Solutions Specialist is a Registered Apprenticeship. TEKIMAX can sponsor it for your people, or deliver the related instruction for a programme you sponsor; the hours and sign-offs land on the same record as the work.

  • They learn on real client work, beside a mentor, from the first week
  • They move on by showing what they can do, not by serving out the months
  • TEKIMAX sponsors the programme, or teaches the related instruction for yours
A U.S. Department of Labor Registered Apprenticeship

AI Solutions Specialist

U.S. Department of Labor registered · Competency-based

See the apprenticeship

Talk to us

Tell us what you are building

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.

Would rather write your own email? [email protected]

What brings you here?

Four questions, then we find a time. You see the Studio on real work rather than a slide, and you can ask what it does when something goes wrong.

Where the reply goes.

Optional.

A rough number is fine.

Govern the build. Augment the human.

Tell us what you are building and who asks you about it. We will show you the record it would leave behind.