Project Gemmate � Multi-Model AI Image Generation & Jewelry Analysis Platform
Work/Case Studies/Project Gemmate
Generative AI & Computer VisionVerified Commercial SaaS — Gemmate ai / JewelPro.ai
Project Gemmate

Multi-Model AI Image Generation & Jewelry Analysis Platform

Orchestrated Vision Models, LLMs, and Image Generation models into a production-grade B2B SaaS for fine jewelry design, featuring real-time progress streaming via Server-Sent Events (SSE) and strict credit governance.

Engagement Scope: AI Multi-Model Orchestration & Real-Time Streaming Architecture

Architected the core AI orchestration pipeline, Server-Sent Events streaming engine, and Redis rate-limiting/credit infrastructure.

System ArchitectureProduction EngineeringPerformance Optimization
01The Problem & Business Challenge

Generating fine jewelry designs requires chaining Vision models (to analyze customer reference sketches), LLMs (to parse structural manufacturing constraints), and Image Generation models. Synchronous implementations suffered from frequent HTTP timeouts, dropped renders, high GPU costs, and vulnerability to API abuse.

02The Architectural Solution

Designed an asynchronous orchestration state machine with real-time feedback. Leveraged Server-Sent Events (SSE) to stream generation progress directly to the frontend without timeouts. Implemented Redis token-bucket rate limiting, organization credit pools, automated circuit breakers, and lossless edge image compression for 4K renders.

03What We Built & Execution Details

Built custom LLM prompt normalization translating jeweler terminology into exact geometric parameters. Engineered the SSE progress dispatch pipeline. Integrated in-memory credit validation before GPU compute dispatch to protect margins. Automated WebP conversion reducing 10MB renders to sub-500KB without optical facet degradation.

04Engineering Challenges & Trade-Offs

Chaining multiple disparate AI models sequentially without accumulating latency or losing state; streaming real-time progress through edge reverse proxies; preserving micro-level diamond facet reflections during automated image compression.

Measured Commercial Outcome

Zero browser timeouts across multi-stage generative pipelines; instant visual progress feedback via SSE; robust credit protection preventing quota exhaustion; platform successfully deployed to fine jewelry designers and manufacturers.

Documented metric during engagement period
My Contribution & Role
Lead AI Systems Architect & Backend Engineer

End-to-end technical leadership: domain modeling, system architecture, database design, hands-on production code, and zero-defect deployment.

Technologies Deployed
Node.jsVision ModelsStable Diffusion / SDXLServer-Sent Events (SSE)RedisDocker
Related Blueprint
Inspect Technical Architecture
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Confidential Engagement

Presented under fictional portfolio codename. Specific commercial entities and confidential operational identifiers have been withheld to respect non-disclosure obligations.

“Building an AI platform for the jewelry industry required more than just connecting an API; it needed complex LLM orchestration, vision analysis and enterprise-grade stability. Shubham and his team took full ownership and turned my scattered ideas into a robust, real-time AI powerhouse.”
Founder
Founder • JewelPro.ai / Gemmate ai
Verified Engagement
Absolute IP Ownership

You own the system.
The code. The infra. The data.

Software should not become valuable enough to matter and then be impossible for the client to own or operate.

Everything I build is 100% client-owned intellectual property from day one. You own the code, the accounts, the infrastructure, and the data. No vendor lock-in.

What belongs to you in full:

  • 01Source code & Complete Git Repositories
  • 02Apple, Google & Enterprise Developer Accounts
  • 03Cloud Infrastructure, VPCs & Production Databases
  • 04Figma Design Files, Assets & Documentation
  • 05CI/CD Deployment Pipelines & Signing Keys