Capability stays trapped with individuals
Prompts, workflows and context live inside personal accounts. Every person starts again, quality varies, and useful improvements do not spread across the company.
Your company already uses AI. It just doesn't have a system.
Turn scattered AI use into one company operating system.
We install your company-owned AI operating system with one team in 4-6 weeks. So knowledge compounds, workflows improve, and leadership can measure what AI is actually producing.
The rhythm
Work is packaged in the evening, built overnight, reviewed each morning.
work packaged in the evening
built overnight
reviewed each morning
acceptance review complete
new model absorbed, nothing reset
context updated
system check: green
morning review ready
only accepted work counted
queue picked up
skills shared across the team
standards applied on every build
keys in the Keychain, never in chat
everything reversible
meter refreshed
workflow tuned
The problem
AI adoption is happening person by person. Company capability is not.
Your team already has access to powerful AI tools. But each person uses them differently, valuable context stays trapped in private chats, and improvements disappear when the person who created them moves on.
The result is activity without compounding capability: more tools, more experimentation, and no shared system leadership can govern or measure.
Leadership cannot prove the return
Licences are active and usage is growing, but there is no baseline, no accepted output measure and no credible answer when leadership asks what changed.
Experimentation creates risk without scale
The fastest employees create unofficial systems around company controls. More cautious teams wait. The company gets fragmented adoption, inconsistent standards and no shared path forward.
The install
We install your company's AI operating system.
We start with one team, up to ten people and three high-value workflows. Over four to six weeks, we install the operating layer, connect it to your existing tools and help your team turn its working knowledge into reusable company capability.
It runs on your accounts, inside your agreed data boundaries, and remains owned by your company.
Choose the first team and workflows
We examine how the team currently works, identify up to three workflows with measurable value, establish the baseline and agree what success must look like before anything is built.
Define boundaries and acceptance criteria
We document the approved tools, accounts, data boundaries, human review points and acceptance criteria. The operating rules are agreed before the system runs.
Install and train the operating system
We install the shared operating layer on your accounts, encode the team's standards and connect the selected workflows. Your team works alongside us so the system reflects how the company actually operates.
Measure accepted output
The meter compares the agreed baseline with accepted work produced after installation. We track cycle time, shipped work, rework and hours returned, not model activity or generated volume.
You pay your AI providers directly. We never mark up your AI spend.
The starter pack is the whole system at its smallest honest size, installed on one Mac, in one sitting, owned by the person it is installed for. A guided installer does the work; you make the calls.
Illustrative output. The commands are the kit's own; copy any of them.
One person first. Then the team. The company install is this system multiplied: a partner for every person, a build fleet that works overnight, one company repo everyone's partner draws from. Feel the difference on one Mac before you scale it across a team.
What you get
The five parts of your AI operating system.
One shared operating system, installed around your team's real work and owned by your company.
Persistent context for each person
Each person gets an AI working layer that retains relevant context, tracks open work and follows the standards defined by the company system.
Automated build capacity for the team
Selected work can be prepared, delegated and processed outside the working day, then reviewed by the team against agreed acceptance criteria.
An operating layer that holds it together
Routing, memory, permissions, review points and improvement loops connect the system. New models and tools can be introduced without rebuilding the company's operating logic from scratch.
Shared company capability
Standards, workflows, playbooks and reusable skills live in one company-owned repository. When one team improves the system, that improvement can be reused elsewhere.
An honest meter
The system measures accepted work against the agreed baseline. It shows whether cycle time, output, rework or returned hours actually changed.
What it is not
- ✕Not a training course. Skills decay; systems compound.
- ✕Not a seat licence. Seats plateau.
- ✕Not a slide deck about transformation.
Your team builds its own system with us, on your machines, inside your data boundaries. You pay your AI providers directly. We never mark up your AI spend.
Proof
Measure the work, not the AI activity.
Most AI programmes report licences, usage or generated output. None of those prove that the company is working better.
We establish the baseline before installation and measure accepted work after go-live.
Depending on the workflow, the meter can track cycle time, accepted shipped work, rework rate and hours returned. Generated volume does not count. Only work accepted by the team counts.
See our system operating in public →Illustrative panel. Your meter starts at your baseline and counts only work your team accepted.
Founding terms
Founding terms. First 3 companies.
A production installation for one team of up to ten people and three agreed workflows. Includes baseline definition, system setup, workflow implementation, team enablement and the first meter report.
Monthly system review, workflow tuning, model and tool updates, meter reporting and office hours for six months.
Your company retains the system, workflows and operating assets created during the engagement.
Security
Security, plainly.
Your data stays inside boundaries we agree in writing before anything runs. Provider no-training settings, access controls, audit logs, human approval on every code change, and your IP stays yours.
Full security appendix on request.
- ✓Boundaries in writing
- ✓No-training settings
- ✓Access controls
- ✓Audit logs
- ✓Human approval on every code change
- ✓Your IP stays yours
Built by an operator, not an AI transformation firm.
AI Engineering Team was founded by Marton Gaspar, a product leader with more than ten years of experience across AI, product systems and high-pressure delivery environments.
He has led product work after HomeX's $90M Series A, coached more than 100 product leaders, and worked with teams across the NHS, Department for Education and EY.
The company itself runs on the same operating system it installs: company-owned, continuously improved and measured in public.
Next step
Identify the first team and workflow.
In one call, we examine where AI use is currently fragmented, identify the strongest first workflow and decide whether a four-to-six-week installation would produce measurable value.
or write to marton@martongaspar.com