GEN✴︎LAB is a full generative production platform: a node-based canvas, 22 production tools, and 31 frontier image and video models behind one interface. A product photo goes in; a finished campaign comes out: stills, variants, animated spots.
It runs live in production behind a private gate. Six provider APIs, infrastructure built end to end, and zero client work used to train external models.
The workflows below are concept briefs run on familiar retail products, a deliberate stress test: if the pipeline holds a real package design, real typography, and a real brand system, it holds anything.
The Flow Canvas: One Photo In, a Campaign Out
GEN✴︎LAB's Flow tool is a node-based workflow builder for creative production. This graph starts with one handheld iPhone photo of a sneaker and branches into an entire social kit: isolated on white, floating studio hero, on-feet action close-ups, an outdoor-court relocation, and three animated clips rendered through SeeDance 2, Veo 3, and Gemini Omni Flash in parallel. Any output wires into any input: image, video, frame, or text. A brief becomes a graph the team can rerun, tweak, and reuse.
The SKU Swap: Master File to New Variant
The classic content supply chain problem: a finished hero spot exists for one SKU, and the brand needs the same spot for another. Traditionally that's a reshoot or a week of post. Here, the existing Glacier Mint video and the new Smooth & Silky packshot enter the graph together. A Frame Grab node pulls the key frame, Nano Banana Pro swaps the product while preserving the layout, the "NEW" banner, and the composition, and SeeDance 2 re-renders the motion using the original spot as reference.
A finished master becomes an on-brand variant for a new product line in minutes. Variants stop being a production bottleneck, and time-to-activation goes from weeks to a working session.
One Brief, Every Model
Creative Direction by Comparison
No single model wins every brief. Multi mode fans one prompt and one product reference across five image models simultaneously: FLUX 2 Pro, SeeDream 4.5, GPT Image 2, and both Nano Banana generations. The results land side by side, ten on-brand environment concepts in a single run, and the art direction conversation starts from evidence instead of a single roll of the dice.
- 31-model registry, one interface
- Swap models without re-prompting
- Batch + Multi + Campaign ratios
- Side-by-side results in one grid
UGC Creator: Brief In, Creator Video Out
Performance marketing eats creative variants for breakfast, so UGC has its own dedicated pipeline. A structured brief goes in and an authentic, lip-synced creator video comes out, with casting, scripting, and compliance handled along the way.
The Structured Brief
Everything starts the way a real engagement starts: brand, audience, campaign goal, and key benefit, captured in a structured form. The vertical selector picks the compliance ruleset, casting realism dials between everyday and polished, and environment chips set the scene. Brand guidelines and brief PDFs drop straight in and are parsed into the brief.
- Vertical picks the compliance ruleset
- Casting realism control, anti-glam by default
- Reference uploads parsed into the brief
Script + Casting Variations
Each concept arrives production-ready: a shot-by-shot script with dialogue, visual direction, on-screen text, and timecodes, plus a reference set of talent variations holding the actual product in the actual setting. Compliance reads as chips, not meetings: brand mention by shot two, one clear benefit, a natural CTA.
- Shot-by-shot script with timecodes
- Diverse hero-talent variations per concept
- Optional b-roll set per concept
The One-Take Video
Pick the hero shot and the whole script becomes one continuous, lip-synced selfie take: same face, same wardrobe, same product, no cuts. The generation prompt locks identity across stills and video, a neutral-wording toggle softens regulated-vertical language before it can trip ad moderation, and the result downloads ready for paid social.
- Full-script single take, lip-synced
- Identity locked from still to video
- Neutral wording for regulated verticals
The same engine batch-produces creator-style stills from any packshot: different faces, different rooms, product held true to its real packaging.
Review Built In
Every Asset Knows Its Own Story
Generation without review is a dead end, so the review layer is native. Every asset opens into a lightbox with its full provenance (prompt, model, provider, quality tier, dimensions, timestamp), plus a Frame.io-style notes thread with drawn annotations for feedback. From the same panel, an approved still chains straight into video, recomposition, or a recreate with tweaks.
- Notes + drawn annotations per asset
- Full prompt and model provenance
- Projects group assets by campaign
- Turn to Video / Recreate / Comp chaining
Version 1: The Foundation
2025 · Where the platform started
GEN✴︎LAB grew out of a custom toolset built to replace fragmented subscriptions and manual workflows: image generation, video animation, audio production, and media optimization consolidated into one hub with direct API integrations. V1 proved the thesis. V2 turned it into a platform.
Why Build Custom
Data Sovereignty
No client asset, unreleased campaign, or proprietary work ever passes through a consumer AI product. Every call routes through my own infrastructure to zero-retention provider endpoints, so nothing is kept by the providers or used to train their models.
Speed
A brief becomes a node graph, and the graph becomes assets. Tasks that required switching between apps, exporting, re-importing, and manual adjustments run end-to-end on one canvas, from generation to reviewed, approved variant in a working session.
Model Independence
Thirty-one models across six providers behind one interface. When a better model ships, it slots into the registry and every workflow inherits it. No retraining the team, no replatforming, no waiting on a vendor roadmap.
Control
Custom system prompts, tuned parameters, reference-image pipelines, and baked-in production standards keep output consistent and on-brand, without relying on generic platform defaults.
