Four types of AI production infrastructure. Video, image, film, and character systems — each built around the specific output it produces, the model best suited to it, and the automation layer that removes human bottlenecks.
Narrative video, b-roll, lip-sync, and product loops. Multi-model routing across Seedance 2.0, Kling 3.0, and Veo 3.1 — matched to output type and quality target.
Photoreal characters, editorial illustration, product imagery, and typography. Per-task routing across Flux Kontext, Nano Banana Pro, Seedream, Imagen 4, and Recraft V4.
Full cinematic pipeline from character brief to delivered shot sequence. 180° rule, OTS blocking, consistency across frames. Nine-phase production recipe, brief to CDN.
End-to-end character development. Concept brief → reference set → LoRA training → dual-reference injection → multi-model generation → QC → delivery. Consistent identity at scale.
No single AI model wins every task category. Routing decisions — which model handles which job — are the biggest lever on quality and cost in any production pipeline.
Every pipeline I build has an explicit routing layer: model selection, fallback logic, quality thresholds, and cost targets per output type. That's the work the models don't tell you about.
Photoreal character
Color precision + multi-char support
Flux Kontext + Nano Banana Pro
Cinematic illustration
Highest detail, cheapest per asset at 2K
Seedream 4.x
Narrative video (dialogue)
Multi-shot syntax + dialogue confirmed
Seedance 2.0 via kie.ai
Talking head / lip-sync
Best-in-class lip sync + motion control
Kling 3.0 Omni
Vector + illustration + icons
Only model that does true vector output
Recraft V4
Character LoRA training
$2.40–$8 per run, 20-image bundle
FAL trainers
Tell me the brief — what you need to produce, at what volume, with what quality bar. I'll tell you how I'd build it.