Case study 01Dragon Sourcing2026—now
Zourvex
AI supplier sourcing, from discovery to RFQ
Visit zourvex.com- Role
- Backend & AI pipeline engineer
- Company
- Dragon Sourcing
- Period
- 2026 — present
- Stack
- Python, Django / FastAPI, AI / LLM APIs, PostgreSQL, Next.js / React
Concept visualization — the platform is proprietary, so this illustrates the system rather than reproducing it.
Overview
Zourvex is an AI-powered supplier-sourcing platform under Dragon Sourcing that takes procurement teams from a sourcing requirement to a qualified, defensible shortlist ready for RFQ. I work on the backend and the AI pipeline behind it.
The problem
Procurement teams struggle to turn a broad supplier search into a qualified shortlist they can defend. The validation work between discovery and RFQ is slow, manual and hard to justify later.
The approach
One connected workflow: define the requirement in plain language, discover and validate suppliers, confirm who can actually be contacted, pre-screen with an RFI and advance qualified suppliers to RFQ. AI does the groundwork — refining search language, processing supplier websites, classifying relevance — while the buying team controls every decision.
Contributions
- 01Building backend services and APIs for the sourcing workflow in Python (Django / FastAPI) on PostgreSQL.
- 02Developing the AI pipeline for supplier discovery and validation — refining search language, processing supplier websites and classifying relevance signals.
- 03Implementing the workflow logic that moves suppliers from discovery through contact readiness and RFI pre-screening to RFQ.
Technologies
- Python
- Django / FastAPI
- AI / LLM APIs
- PostgreSQL
- Next.js / React
In the market
Zourvex sits under Dragon Sourcing, a global sourcing and procurement services company founded in 2004 with a presence in more than 50 countries. The platform is built for enterprise and mid-market procurement teams, and for tail-spend categories.