Generative cinema engine

Feature-length motion comics, from a twenty-line premise.

CineAgent is a multi-agent production pipeline that writes, illustrates, voices and composites long-form motion comics, keeping every character consistent across thousands of panels. It runs on NVIDIA GPU infrastructure.

Final frame: Composited panel, as it appears in the finished scene. — Composited panel, as it appears in the finished scene.

180min

Continuous runtime generated from a single premise

2,160panels

Per feature, with locked character identity

<20ms

Dialogue-to-frame alignment accuracy

~5.5hrs

Wall-clock render on an 8-GPU node

01Pipeline

Four stages, one continuous pipeline.

Story, image, sound and camera are handled by specialised agents that share state, so a change to the script carries through to every frame that depends on it.

Expands a short premise into three acts and 54 scenes. A persistent character bible keeps names, motives and appearance stable for the full runtime, so the story does not drift in its third hour.

Recursive act → sequence → scene expansion
Vector-memory character bibles
Scene beats and dialogue passes

Stage 01json

{
  "act_decomposition": {
    "act_I":   { "scenes": 12, "pacing": "inciting_incident" },
    "act_II":  { "scenes": 26, "midpoint": "cipher_revealed" },
    "act_III": { "scenes": 16, "resolution": "sector_climax" }
  },
  "character_bible": "sha256:9b4a7d18c3ef",
  "identity_tokens": ["KIRA_01", "MARCUS_DETECTIVE"]
}

02Technology

Built for NVIDIA GPU infrastructure.

Every stage runs as a GPU worker behind a job queue, so a feature-length project is split across nodes and rendered in parallel rather than frame by frame.

Stage	Model	Hardware	Acceleration	Status
Illustration	FLUX.1 [dev] 12B rectified-flow transformer	RTX 4090 (24 GB) / A100 (80 GB SXM4)	Headless ComfyUI workers, BF16, FlashAttention-2, torch.compile	Production
Depth and segmentation	Depth Anything 3, SAM 2	Tensor Core GPUs, FP16	Half-precision kernels, Triton-optimised attention, zero-copy buffers	Production
Speech alignment	WhisperX (Silero VAD + Wav2Vec 2.0 CTC)	L40S / RTX 4090	CTC decoding parallelised across CUDA streams	Production
Inference optimisation	TensorRT-LLM, TensorRT diffusion engines	Hopper / Ada Lovelace	FP8 / INT8 post-training quantisation, kernel auto-tuning	Roadmap

03Showcase

From premise to finished scene.

Follow one scene through the pipeline: the written premise and the character bible extracted from it, the layers recovered from each panel, and the composited result.

Whoever wiped the sector nine archive left no residue. They took the genesis kernels.

Whoever wiped the sector nine archive left no residue…

−23 LUFS−1 dBTPScore ducked under dialogue

Move the cursor over the frame to see the parallax between the character and background planes.

04Economics

A different cost structure for animation.

Traditional motion comics are priced by the drawing. CineAgent is priced by GPU time, which changes what a studio can afford to try before committing to a release.

	Traditional studio	CineAgent
Budget, 3-hour feature	$1.8M – $3.5M	$120 – $195 in GPU compute
Schedule	12 – 18 months	~5.5 hours wall-clock
Crew	40+ illustrators and animators	One showrunner directing the pipeline
Revisions	Weeks of redrawing per change	Regenerate affected scenes in minutes

Estimate a slate

Adjust runtime and episode count to compare a traditional production budget with CineAgent compute.

Runtime per episode180 minEpisodes1

Traditional studio

$2.25M

~14 months

CineAgent compute

$153

~5.5 hours

Difference$2.25M

Studio figures use an industry benchmark of about $12,500 per finished minute. CineAgent figures cover GPU compute only and exclude staff, licensing and distribution.

05Company

The team.

CineAgent is built by engineers from film production and machine-learning systems, working on the problem of long-form visual storytelling.

Founder Name

Founder & Chief Executive

Two or three sentences on relevant background: what you built before, where, and why you are the person to build this.

Co-founder Name

Co-founder & Head of AI Systems

Two or three sentences on technical background: distributed training, inference optimisation, generative vision, and any published work.