Search by what you want to see
Type a description in plain English — "dramatic golden hour portrait with film grain" — and pgvector finds prompts that produced visually similar images.
Case Study 05 · AI Build
Promere: Prompts As Data,
Not Guesswork
AI image creators burn hours on prompt trial and error, and a prompt that works in one model rarely works in another. Promere is the intelligence layer underneath: search by what you want to see, reverse-engineer any image into a recipe, and reformat it for 10 models.
Library
6,800+
Images classified
Sub-second semantic search
Portability
10
AI models supported
One recipe, formatted per model
Build
$120
In API credits
Solo, in 4 weeks
01 · The Problem
AI image creators waste hours on prompt trial-and-error. You see a generated image you love, but you don't know what produced it — and even if you find a prompt that works in Midjourney, it doesn't translate to Flux or Seedream.
There's no shared intelligence layer for prompts: no way to search by what you want to see, no way to extract a recipe from an image, no way to organize what's working across models. Every D2C marketer, content creator, and AI artist rebuilds the same wheel with every project.
02 · What I Built
Promere is a prompt intelligence platform built on three core capabilities: semantic search across 6,800+ classified images, reverse-engineering any image into its 8-element prompt recipe, and model-specific formatting that translates the same recipe across 10 different AI models.
Where existing tools are either prompt galleries or generation engines, Promere is the layer underneath — the structured intelligence that makes prompts portable, searchable, and reusable across models. Built with semantic vector search on top of a classified visual taxonomy, it treats every prompt as data, not text.
Key decisions: chose pgvector over a dedicated vector database for cost simplicity, used Claude Sonnet for reverse-engineering because prompt extraction quality is non-negotiable, and built the entire platform single-handed in Cursor with Claude as the architecture partner.
03 · How It Works
5 Steps,
End To End
Reverse-engineer any image
Upload a reference image and Promere breaks it into 8 elements: subject, lighting, style, composition, mood, technical settings, color palette, and negative prompt.
Format for any model
Same recipe, different syntax. Switch between Flux, Midjourney, Stable Diffusion, DALL-E, Nano Banana Pro, Seedream, Grok, and three more — each formatted to that model's prompting conventions.
Build your library
Save prompts, organize by collection, search your saved arsenal, and access from anywhere.
Learn the vocabulary
A visual glossary teaches what "anamorphic," "subsurface scattering," and "golden hour" actually look like — with real examples.
04 · Proof
| AI images classified | 6,800+ |
| AI models supported | 10 — Flux, Midjourney, SD, DALL-E, Nano Banana Pro/2/Flash, Seedream 4.5/5 Lite, Grok, Ideogram |
| Prompt elements per image | 8 — subject, lighting, style, composition, mood, technical, color, negative |
| Search-to-result latency | Sub-second on semantic search |
| Build cost | $120 in API credits, single-founder execution |
05 · The Stack
| Frontend | Next.js 15 · React · Tailwind CSS · Lucide React · Recharts |
| Backend & data | Supabase (Postgres + pgvector) · Auth + Row-Level Security |
| AI models | Claude Sonnet (reverse-engineering) · OpenAI text-embedding-3-small (semantic vectors) · Claude Haiku (classification) |
| Storage & infra | Cloudflare R2 (image storage, 5,886 WebP thumbnails) · Vercel (hosting + edge functions) |
| Built in | Cursor with Claude as architecture and code partner |
Supabase + pgvector eliminated the cost and complexity of a dedicated vector database. Cloudflare R2 made image storage essentially free at scale. Claude Sonnet was non-negotiable for reverse-engineering quality — the prompt extraction has to be accurate or the entire feature collapses.
06 · What’s Next
- Launching publicly across r/StableDiffusion, r/PPC, and Product Hunt to validate which audience converts first: AI artists looking for prompts, or D2C marketers scaling ad creative.
- Building user submission for community-contributed prompts, model comparison views (same prompt across 10 models, side by side), and an API layer for ComfyUI and n8n integration.
- Long-term, Promere becomes the connective layer between prompt creation, model execution, and workflow automation — the intelligence platform underneath every AI image workflow.
07 · Takeaway
Treating every prompt as structured data, not text, is what makes it searchable, portable and reusable. It's also proof of the builder half: a production app, designed, built and shipped end to end by one operator.