If you are building an AI app builder, your architecture decision happens early:
The first option looks simpler. The second option scales.
This post explains why we use a scene-based runtime model at Dreams.fm.
The architecture trap in many AI builders
Many teams ship quickly with this pattern:
It works for small demos.
Then real requirements arrive:
Prompt-response architecture collapses because state transitions are implicit and fragile.
Scene-first runtime model
We treat each project as scenes plus timeline state.
A scene includes:
Every edit becomes a transform against scene state.
That gives us:
Why scene graphs work for AI app builders
Scene graphs solve three practical problems.
1. They preserve hierarchy
Generated output is not a flat blob. It has dependencies, constraints, and parent-child relationships.
Scene graphs make this explicit, which makes targeted updates safer.
2. They support multi-surface projection
The same scene can render to different outputs:
Without duplicating business state.
3. They make branching real
Teams can fork scene state, test alternatives, and merge decisions based on outcomes. That is essential for iterative product work.
State forking and timeline memory
Forking is not a convenience feature. It is how teams reduce risk.
With timeline memory:
This matters most when AI-generated and human-authored changes coexist.
Input parity in a scene runtime
Multimodal input only works when every input type resolves to typed transforms.
Useful commands can come from text, speech, structured action calls, or direct editor actions. All of them should end in the same runtime semantics.
Examples:
When inputs map to typed transforms, teams keep control.
Runtime design principles we follow
For a production AI app builder:
Where this meets SEO and product strategy
People search for "ai app builder" because they want end-to-end execution, not isolated generation.
Architecture content attracts qualified users evaluating platform depth and migration risk. It also creates durable organic value because these decisions persist beyond short release cycles.
Closing
If you are serious about building an AI app builder, architecture is your product.
Scene graphs, timeline memory, and transform-based execution are the systems that keep your platform coherent as use cases expand.
This is the foundation behind Dreams.fm and fmEngine as private beta scales.
