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Capability · 01

01 · Practice

Applied AI

Custom generative pipelines, supervised on set.

Applied AI

Definition

Generative craft for the picture. Custom pipelines, model fine-tuning, on-set AI supervision, and brand-trained tools, built for productions where the AI work has to land at broadcast and theatrical spec. Output is auditable, repeatable, and owned by the production.

For

  • 01Brands building proprietary content engines.
  • 02Agencies bidding work that requires AI as the craft, not a buzzword.
  • 03Studios and networks pressure-testing IP across model generations.

What we ship

04 sub-practices

Each line below is a standing offer. Scoped, priced, and led by a named practice lead. Mix and match across a brief; nothing here is a one-off.

Custom Model Training & Fine-Tuning

LoRA, DreamBooth, and full fine-tunes on brand archives, talent likenesses, product geometry, and house grammar. Versioned, governed, and retrainable as the brand evolves.

FluxSDXLComfyUIWanHunyuan

Generative Pipeline Engineering

Deterministic, seed-locked pipelines wired into the production stack, from text-to-image to text-to-video to AI relighting and inpainting, with guardrails the production can sign off on.

ComfyUIRunwayVeoKlingSoraTopaz

On-Set AI Supervision

A named AI technical director embedded from prep through wrap. Real-time generation on monitor, clean-plate prep, AI-assisted continuity, and live integration with the DP, director, and 1st AC.

Custom on-set toolingResolveNuke

Synthetic Content & Likeness

Talent likeness models, synthetic stand-ins, and dub-ready performance capture, built under explicit, contract-cleared rights and a documented chain of custody.

Custom likeness pipelinesElevenLabsRunway Act-One

Outcomes

  • A pipeline you own, not a subscription you rent.
  • Auditable provenance from prompt to master.
  • Frame-accurate consistency across versions, regions, and re-uses.

Process

  1. 01Discovery: read the brief for where generative tooling earns its place.
  2. 02Pipeline design: pick or train the right models; lock seeds, prompts, guardrails, and rights.
  3. 03On-set supervision: AI lead embedded from prep through wrap.
  4. 04Hand-off: versioned pipeline + documentation delivered with the master.

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