Brand Builder

The problem: visual brand drift

Ask an image model for the same product in 3 different advertising contexts and you get 3 different products. The bottle changes silhouette. The label typography drifts. The finish goes from matte to gloss. Each image is individually good and the set is useless, because a campaign is only a campaign if the thing being advertised is recognisably the same thing.

Brand Builder takes a product concept and produces a coherent multi-medium campaign: highway billboard (16:9), broadsheet newspaper (3:4), social post (1:1). The technical problem is entirely consistency.

2-phase conditioning, not better prompting

My first instinct was to describe the product more precisely in each prompt. That does not work — text alone leaves the model too much freedom.

What works is anchoring every render to a single generated image:

1. Brand DNA synthesis. Name, tagline, category, materials, packaging silhouette and explicit hex colours become a fixed set of physical tokens — “fluted cylindrical glass dropper”, “sandblasted titanium”, “amber glass with gold foil debossing”.
2. Master studio packshot. One unadorned shot of the product on a neutral plinth. This is the anchor.
3. Medium-specific synthesis. The master image’s raw base64 buffer goes into the model’s multimodal input alongside the physical tokens, with the instruction to place this exact product into the target medium.

The text tokens hold the description steady; the image conditioning holds the geometry steady. Neither alone is enough.

The guardrail that took the most iterations

The brief required no people in any shot. Advertising prompts hallucinate people relentlessly — commuters on the highway, hands holding the bottle, models in the metro station. It took 3 layers:

• An explicit negative constraint in the system prompt, stated in absolute terms and naming the specific failure modes: hands, faces, silhouettes, pedestrians.
• Scene sanitisation. Billboards are prompted at twilight on empty highways. Newspapers as flat-lays on a wooden table. Transit as an empty architectural terminal. Choosing scenes with no natural reason to contain people does more work than forbidding people.
• A prompt inspector in the UI, so the constraint can be verified as transmitted on every generation rather than assumed.

That last one matters more than it sounds. A guardrail you cannot audit is a guardrail you are hoping for.

Failing without crashing

Image generation on the free tier has a quota of zero, so the interesting path is the failure path. The server intercepts RESOURCE_EXHAUSTED and 429s and falls back to a deterministic SVG mockup rendered with the user’s exact brand palette, packaging geometry and typography for the chosen medium. The client receives a structured isQuotaNotice response and shows a banner explaining that live rendering needs a billing key.

The user still sees their campaign laid out, still interacts with it, and understands exactly why it is a mockup. Nothing hangs and nothing crashes.

Stack

React + Vite client, Node/Express proxy, TypeScript throughout. Gemini image generation through the official @google/genai SDK. The API key never leaves the server; the client only ever calls /api/imagine and /api/suggest-product.

What I learned

Consistency is an architecture problem, not a prompting problem. Every attempt to solve drift by writing a better description failed. Passing a reference image solved it.

Negative constraints work better as positive scene choices. “No people” is a rule the model can miss. “Empty highway at twilight” is a scene where a person would be strange. The second survives more generations than the first.

Design the quota-exhausted path first. On a free tier it is the common case, and the SVG fallback became the part of the app I was most pleased with — the product stays useful at its least capable.