Pure AI, or hybrid. Whatever the work needs.
Our pipeline treats live action, AI generation, and traditional post as three independent capabilities that can be used alone or combined. Plates feed models. Models feed comp. Comp feeds finish. One creative direction holds it together whether the work is fully generated or shot on set. We also design production-ready AI pipelines for agencies, studios, and brands who need to run this in-house.
Pipeline design
An architecture for how generative work moves through your existing production and approval structure.
Model strategy
Tiered model selection per task and per budget, rather than one tool used for everything.
Tooling
Internal tools for the repetitive parts — batching, versioning, review, and handoff.
SOP and handover
Written standard operating procedure for the pipeline we built, so it runs without its designer present. Broader team training and strategy sit under AI consulting.
- 01
Map the current state
How work actually moves today, including the informal parts nobody documented.
- 02
Design
Where generation belongs, where it does not, and what the approval gates are.
- 03
Pilot
Run one real project through it and fix what breaks.
- 04
Document and hand over
Written SOP, tooling, and training for the people who will run it.
What is a hybrid AI production pipeline?
A production workflow in which live-action photography, generative models, and traditional VFX and finishing are separate stages that feed one another, rather than competing approaches. Plates can condition generation; generated elements can be composited like any other element; everything converges in a conventional finishing pipeline. The advantage is that each shot can use whichever combination is cheapest and best.
Can you build a pipeline for our in-house team?
Yes. This is a distinct engagement from production work: we map how your team operates now, design where generative work fits, pilot it on a real project, then hand over documented SOP and tooling. Our founder authored the production SOP used across every engagement at a previous AI production company, so this is well-worn ground. If you want assessment and recommendations without the build, that is an AI consulting engagement instead.
Why not just use one model for everything?
Because models differ sharply in what they are good at, what they cost, and what they are licensed for. A tiered strategy — cheap models for exploration, better ones for approved directions, and the most controllable for final — costs less and produces more predictable results than committing everything to a single tool.