Raven AI Stylist
A conversational shopping stylist that gives personalised outfit advice and shows how a look might appear with AI-generated try-on images — powered by a model trained specifically for fashion.
Overview
Raven shows that a model trained for a specific domain, wired into a well-designed multi-agent system, can make recommendations feel genuinely personal.
You chat with a stylist that understands your taste and occasion, get concrete outfit suggestions, and see AI-rendered try-on images of the looks — not just descriptions. Behind the scenes a general model runs the conversation while a fashion-specialist model handles the expert advice, so replies stay both natural and on-point.
How it works
- 1
A model fine-tuned for fashion
A base open model (GPT-OSS-20B) is fine-tuned with LoRA (via Unsloth, 4-bit) into a domain adapter — "sigmoi" — that gives sharper, more relevant style guidance than the general model alone.
- 2
Orchestrator + specialist agents
A stateful orchestrator on the general model coordinates the conversation and calls stateless specialist agents (built with LangGraph): the base model routes and the sigmoi adapter specialises — same endpoint, selected by name.
- 3
AI virtual try-on
A dedicated agent composes a prompt and renders a photorealistic try-on image with Gemini Flash Image, so you can see the outfit rather than imagine it.
- 4
Served in the cloud, streaming
The model runs on an OpenAI-compatible vLLM endpoint on AWS; the app streams replies token-by-token for a real-chat feel.
- 5
Graceful degradation
If the inference endpoint is unavailable, the app stays up and the stylist shows a warm "temporarily unavailable" message — the rest keeps working.
Architecture
An orchestrator + stateless-sub-agent pattern. The browser talks only to the orchestrator; the orchestrator calls the sub-agents privately.