AI Image Generation for Incontinence Product
Budget: $30 – $250 USD
Custom AI Image Generation Tool for DTC Incontinence Product Modeling
We sell incontinence underwear DTC and need a custom internal tool to generate product model images at scale using AI.
Off-the-shelf AI tools don't work for us — they change the product details (wrong seam placement, wrong padding shape, wrong fabric look).
So we need a purpose-built system that locks in product accuracy through structured product profiles.
What you'll build:
A web-based tool where we define a product and a scene direction, kick off a batch, and come back to a gallery of 30+ generated images to pick from.
The core is a Gemini Pro image generation engine (not Flash — this is hardcoded) backed by a job queue so batches run in the background.
Products are defined through a profile system — fabric type, seam locations, padding description, colorways, fit notes — which the prompt engine reads to assemble accurate generation prompts every time.
Users can also save and reuse prompt templates through a prompt library.
There are 3 generation modes: starting from scratch with just a product profile, adapting poses from an existing product image to a new product, and generating from a saved prompt template.
Tech stack: Node.js or Python backend, BullMQ + Redis for the job queue, PostgreSQL for profiles and job records, S3 for image storage, React frontend.
Scope: ~20 hours
This is a greenfield build — there's a prior version that exists for reference but this is a clean rebuild with a different architecture.
We sell incontinence underwear DTC and need a custom internal tool to generate product model images at scale using AI.
Off-the-shelf AI tools don't work for us — they change the product details (wrong seam placement, wrong padding shape, wrong fabric look).
So we need a purpose-built system that locks in product accuracy through structured product profiles.
What you'll build:
A web-based tool where we define a product and a scene direction, kick off a batch, and come back to a gallery of 30+ generated images to pick from.
The core is a Gemini Pro image generation engine (not Flash — this is hardcoded) backed by a job queue so batches run in the background.
Products are defined through a profile system — fabric type, seam locations, padding description, colorways, fit notes — which the prompt engine reads to assemble accurate generation prompts every time.
Users can also save and reuse prompt templates through a prompt library.
There are 3 generation modes: starting from scratch with just a product profile, adapting poses from an existing product image to a new product, and generating from a saved prompt template.
Tech stack: Node.js or Python backend, BullMQ + Redis for the job queue, PostgreSQL for profiles and job records, S3 for image storage, React frontend.
Scope: ~20 hours
This is a greenfield build — there's a prior version that exists for reference but this is a clean rebuild with a different architecture.
Related categories:
PHP
XML
Python
Website Design
Node.js
Image Processing
Generative AI
AI Model Development
AI Development
Gemini