Inside Studio Haim
The systems behind the work.
Studio Haim is not just where the films are published. It is also the system used to make them, track them, learn from them and decide what happens next.
Most people talk about what agents can do.
I spend more time designing what they are not allowed to do unsupervised.
AI handles defined parts of the work. The decisions that matter stay human.
Creative work needs a workflow, not just a prompt.
A scene begins as an idea, but getting it to release means keeping writing, generation, continuity, editing, publishing and platform requirements aligned.
The studio uses a structured workflow to move a film from concept to finished release without losing track of what changed along the way.
Each project can carry its own creative DNA: opening, structure, characters, timing, tools, subtitles, release status and the decisions that shaped it.
The goal is simple: make experimentation repeatable without making it mechanical.
I do not treat an AI agent as a person who should “figure everything out.”
Each agent or model gets a defined responsibility, enough context to do that job, and a result that can be checked.
That might mean generating a shot, reviewing code, reconciling publishing data, analysing performance or implementing a specific change.
The important part is not how autonomous the system looks.
It is whether I can tell what it did, why it did it and whether the result is safe to keep.
Anything consequential has a stopping point.
Publishing, spending money, destructive changes and decisions that cannot easily be reversed do not happen simply because an AI suggested them.
The workflow keeps a person in the loop before those actions happen.
This sounds less exciting than “full autonomy”.
It is also much more useful in production.
Performance data is useful only when it answers a question.
The studio tracks releases across platforms and connects those results back to the creative decisions behind them.
Instead of treating every successful video as a new rule, experiments begin with a question.
Does opening directly with conflict hold attention better than opening with context?
The system records the relevant work, the evidence and the eventual decision separately.
A number is evidence.
It is not automatically a conclusion.
When an existing tool does not fit the workflow, I build the missing layer.
Studio Haim has its own internal publishing and performance system.
It connects creative work, release status, platform coverage, performance evidence, experiments and creative DNA in one place.
The publishing and performance systems were built around the same constraints: visible state, explicit approval before publishing, and evidence kept separate from decisions.
The system exists because the studio needed it.
That is generally how I approach digital products too: start with the real operational problem, then build only enough system to make that problem simpler.
AI makes building faster. It also introduces a new class of mistakes.
An agent can write perfectly valid code and still solve the wrong problem.
It can remove something because it looks redundant without knowing why it was created.
It can improve one screen while quietly breaking the workflow around it.
So part of the work is designing the environment around the agent: clear scope, repository rules, validation, observable state and checkpoints before production.
The better the agents become, the more important this layer becomes - not less.
What I actually do
I sit somewhere between creative direction, product thinking, operations and implementation.
I take a messy process, work out what needs to stay human, what can be automated, what should be measured and where the system needs to stop and ask.
Then I build the workflow around it.
Studio Haim is the place where I keep testing that way of working in public.
Selected system principles
The constraints are part of the work.
- Give AI a defined job.
- Broad autonomy sounds impressive. Clear responsibility produces better work.
- Make state visible.
- A system should be able to tell you what is done, what is missing and what is blocked.
- Do not confuse output with evidence.
- A generated answer is something to inspect, not something to believe automatically.
- Keep irreversible actions human.
- Publishing, spending and destructive actions deserve explicit approval.
- Learn deliberately.
- One good result is interesting. A repeated result under a defined experiment can become a decision.
- Build for the actual workflow.
- Do not add infrastructure because it sounds sophisticated. Add it when the work needs it.
The films are the visible output. The system behind them is part of the work too.
I am interested in AI not as a layer added to an existing process, but as something that changes how the process itself should be designed.
That is what I am building and testing here.
If you're working on the gap between capable agents and reliable production systems, I'd be interested in comparing notes.