Commercial Creator

An agentic pipeline that turns a product description into a finished video commercial: creative brief → concepts → script → adversarially-reviewed shot list → styled storyboard keyframes → VO-timed animatic → Seedance 2.0 video generation (with storyboard-composed multi-shot windows) → QC judging with corrective regeneration → assembly → music + expressive VO mix → final cut with themed overlays. A FastAPI server drives it; a single-file web UI and an MCP connector (for Claude Code and other agents) sit on top.

Built with a human-in-the-loop by design: the brief, the shot list and the animatic are approval gates, and video generation never spends money without an explicit approval step — in the UI and in the MCP tools alike.

What it produces

30–60s commercials/trailers at draft (480–720p) or final (1080p) tier, with:

• multi-agent planning: intake strategist, creative director (3 concepts, beat-scripted VO at ~2.3 words/s), a shot room where a cinematographer critic and an editor critic attack the draft shot list in parallel before the director revises it

• storyboard keyframes (Gemini nano-banana via fal.ai) locked to a style recipe, with product reference images for identity

• an audio-first animatic: VO takes are generated and measured first, the timeline stretches to fit, and the shot plan reconciles the locked timeline onto Seedance's 4–15s generation windows (scale / trim / split / multi-shot collapse / storyboard-composed windows)

• Seedance 2.0 generation via fal.ai with per-route handling (t2v / i2v / reference-composed r2v / last-frame chaining), transient retries, a billing fail-fast, and cancellable queue polling

• a 6-dimension vision QC judge that stages corrective regens (never auto-spends) and a self-improvement loop that distills recurring failures into durable prompt rules (per-project lessons + global house rules)

• ElevenLabs v3 expressive VO (audio tags per beat kind, per-brand voice casting by an agent) + generated music bed, sidechain-ducked under the VO

• Remotion-rendered overlays: theme-palette lower thirds and end cards, with a presentation setting (cinematic / balanced / informational) chosen at intake or recommended by the brief agent

Typical draft-tier cost: $10–20 per 30s spot (Seedance dominates; LLM/images/VO are cents). See docs/ for the research that shaped the pipeline.

Related MCP server: ai-promo-video

See it in action

Five finished spots produced end-to-end by this pipeline — each was one product description in, one video out (every brand fictional). Uploaded as demos on The Bearded AI Guy:

🎬 PocketBrew — coffee commercial (45s, ~$17)

The pipeline's first complete spot: theme-styled lower thirds and end card, ElevenLabs music bed ducked under the VO. Every shot generated per-keyframe (image-to-video) from the styled storyboard.

🛸 ScoutFly — FOMO drone commercial (31s, ~$15)

The QC loop on camera: the vision judge failed 4 of 6 draft clips (garbled text in the blueprint theme), staged corrective regenerations with its feedback spliced into the prompts, and every fix passed. That failure pattern then became a permanent theme rule — the self-improvement loop's origin story.

⌚ TIMEPIECE — 60s streaming-series trailer (~$27)

A prestige drama trailer for a show that doesn't exist: custom cinematic theme, tick-synced cuts, a custom-prompted orchestral score, and a measured animatic that landed the 60s target exactly.

☁️ SKYSILVER — action-comedy trailer (31s, ~$14)

The storyboard-composed route at work: two of its montages are SINGLE Seedance generations following a numbered storyboard grid (multiple hard cuts per call, scene-detection validated), roughly halving the cost of per-shot generation. The adversarial shot room made 14 revisions before a dollar was spent — including a new VO-synced cutaway.

✨ Wear Your Sparkles — beauty commercial (33s, ~$17)

The hardest photoreal test (macro glitter, skin, eyes): the judge ran 7 corrective regenerations against glitter-physics hallucinations and distilled two durable lessons about macro realism that now ride every prompt in the project.

Requirements

• Python 3.12 (pydub/audioop compatibility)

• ffmpeg + ffprobe on PATH

• Node.js 18+ (only for the final overlay render via Remotion; every other stage works without it)

• API keys:

  • FAL_KEY — fal.ai (Seedance video + nano-banana images)

  • ANTHROPIC_API_KEY — planning/judging agents

  • ELEVENLABS_API_KEY — VO + music (optional; the pipeline degrades to silent scratch timing without it)

Setup

python -m venv .venv
.venv/Scripts/activate            # Windows; source .venv/bin/activate elsewhere
pip install -r requirements.txt

cp .env.example .env              # fill in your keys

uvicorn commercial_server:app --port 8700
# open http://127.0.0.1:8700  (the web UI)

Health check: GET /api/v1/health reports missing binaries/keys.

Google Drive / synced-folder warning: if this folder lives on a synced drive, set REMOTION_BUILD_DIR to a local path (e.g. C:/remotion-build/projects) — npm install fails on synced filesystems, and renders stage off-drive then copy back. Non-synced installs can set REMOTION_WORK_MODE=project.

The pipeline

intake → brief* → script → shots* → refs → storyboard → animatic* → generate† → assemble → audio → variants
         (*human approval gates)                                    (†explicit spend approval)

Every stage is a job with live logs (SSE to the UI), and artifacts are versioned append-only — editing an upstream artifact marks everything downstream stale rather than deleting it.

Claude Code / MCP connector

mcp_server.py exposes the pipeline as typed MCP tools so an agent can drive it end-to-end:

pip install "mcp[cli]"
claude mcp add commercial-creator -- python /absolute/path/to/mcp_server.py

Then, in Claude Code: "Create a 30-second commercial for " — the agent walks the stages with run_step, honors the approval gates via approve_artifact, and cannot generate video in one step: stage_video_batch returns the dollar estimate, and only the separate approve_video_jobs call spends. Point the connector at a non-default server with COMMERCIAL_CREATOR_URL.

Field notes baked into the code (the expensive lessons)

• Seedance 2.0 has no seed input — reproducibility lives in reference images + verbatim prompts. Consistency comes from style-recipe keyframes, reference binding (@Image1…) and single-generation multi-shot windows.

• The likeness gate: the provider rejects detectable photoreal human faces in reference-to-video reference images and video refs (facial fragments — a macro eye — count). The same images pass as image-to-video start frames. Stylized/animated character sheets pass, and carry identity across composed multi-shot generations remarkably well. The planner routes around all of this automatically via a per-shot face_prominent flag and a per-project visual_mode (photoreal | stylized).

• Storyboard-composed windows: a numbered grid of keyframes (≤3×3, black borders, number badges) as @Image1 + timed Shot N blocks reliably yields one generation with native hard cuts — ~1.2s/shot floor, 6–7 shots per 10s is the readable ceiling. Cut counts are validated by scene detection after download.

• Output-side "sensitive content" flags are noisy — one identical retry is automatic and usually passes. Input-side likeness rejections are deterministic — never retried, rerouted instead.

• ElevenLabs v3 does not support previous_text/next_text, and its stability tiers map to floats (0.5 = natural). The voice is the quality lever: conversational-tagged voices respond to [excited]-style audio tags; narration voices ignore them.

• Assembly policy: never concat stream-copied and re-encoded segments; uniform per-segment encode trimmed to edit duration, 4-frame crossfades on take-chained joins.

📖 Full illustrated walkthrough — installation to finished commercial, with screenshots of every panel (also published as a Medium article). docs/seedance-commercial-workflow-best-practices.md is the full research playbook; docs/seedance-commercial-app-implementation-plan.md is the original architecture spec.

Costs & safety rails

• Per-project budget cap: jobs estimate before running; the queue refuses to start work that would blow the cap (402) counting in-flight estimates.

• All video generation jobs are created needs_approval and survive server restarts without auto-running.

• The QC judge stages corrective regens with feedback spliced into the prompt — approval is still explicit, and after two failed fixes it escalates to a human instead of burning more money.

Repository layout

commercial_server.py     FastAPI app (port 8700): REST + SSE + static UI
commercial/              the pipeline package
  agents/                LLM agents (intake, director, breakdown+shot room,
                         reference, judge, continuation) — Claude via tool-forced
                         structured output
  adapters.py            Seedance capability descriptors + fal queue client
  shot_plan.py           timing contract: animatic timeline -> generation rows
  compiler.py            deterministic prompt compiler (inspectable pre-spend)
  storyboard_sheet.py    numbered grid compositor for composed windows
  keyframes.py/animatic.py/assembly.py/audio_vo.py/audio_final.py/music.py
  overlay.py/themes.py   Remotion overlay render + theme palettes
  jobs.py/store.py       approval-gated job queue + single-writer JSON store
  lessons.py             self-improvement: QC failures -> durable prompt rules
static/commercial.html   the whole UI (no build step)
mcp_server.py            MCP connector for Claude Code
remotion_template/       Remotion project (CommercialFinal composition)

License

MIT — see LICENSE.