AI Ad Creative Platform Development

Reference architecture

1. Brief Input
2. Prompt Engineering
3. LLM + Image Model Orchestration
4. Variant Generation
5. Platform Validation
6. Ad Platform Upload
7. Performance Feedback Loop

A typical AI ad creative system: a brief feeds prompt engineering, the orchestration layer drives LLM and image models to generate variants, platform validation enforces per-channel specs before upload, and the performance feedback loop pulls results back to identify winners and iterate.

Frequently asked questions

• Which image generation models do you have production experience with?+

  We have production experience with the major providers: OpenAI DALL-E, Stability AI, Flux, Replicate-hosted models, and the Midjourney API. We build the abstraction layer so you can route, fall back, and switch models without rewriting your pipeline, and wire in new models as they fit your quality or cost target.

• How do you handle ad platform policy rejections?+

  We validate against platform policy before you submit, so rejections don't surprise you at upload. We build per-platform spec and prohibited-content checks ahead of submission, capture rejection reasons from the ad platform APIs when they occur, and feed them back so the system can regenerate the creative or flag it for review.

• Can you integrate with our existing brand asset library?+

  Yes. We ship integrations with brand asset libraries like Brandfolder and Frontify, plus storage and CDN layers (Cloudinary, AWS S3, Bunny CDN). Brand colors, logos, and typography rules become enforceable rules built into the generation pipeline.

• How do you architect cost control for image generation at scale?+

  Image generation cost is an architecture problem, so we design for it: generation queue management, caching and dedup of repeated requests, model routing to the cheapest model that hits your quality bar, and per-tenant usage metering. You get a breakdown of spend by variant, campaign, and customer, in addition to the monthly total.

• Do you also build the data pipeline behind creative-performance feedback loops?+

  Yes. The feedback loop is core to what we build. We ingest performance data from the ad platforms (Meta, Google, TikTok), attribute it back to specific variants, identify winners on the metrics you care about (CTR, CPA, CPM), and feed those signals into automated iteration. The generation engine and the analytics pipeline are designed as one system.