About
A practical AI cooking system, not a recipe content farm.
My Royal Chef AI combines browser-native inference with cloud models so common cooking questions feel immediate and complex ones still go deep. The product is built for utility, speed, and low-friction decision making in the kitchen.
Platform
What this is.
Core idea
Context-aware cooking help.
It is not a static recipe search page. It understands ingredients, constraints, time, and skill level so the answer changes with the situation.
Built by
Aporia Systems
Created by Yunus Emre Vurgun and Yusuf Ceylan. The focus is practical software with clear systems thinking, not decorative product noise.
Common cooking questions are handled by the local knowledge base for instant responses, while complex requests route to cloud LLMs with deeper culinary reasoning.
Architecture
How a request moves through the system.
1. Input
Text, audio, or image
The interface accepts the form of input that fits the moment.
2. Knowledge layer
Local KB match
Familiar, high-confidence questions resolve instantly from the structured knowledge base without cloud latency.
3. Cloud routing
LLM processing
Complex requests route to Meta Llama 3.1 70B for deep recipe generation, planning, or reasoning.
4. Specialized models
Multimodal fallback
Audio transcribes via Whisper, images analyzed via Llama 3.2 Vision, and complex recipes via Llama 3.1 70B with automatic fallback models.
5. Context shaping
Mode-aware prompting
The request is framed differently for recipes, shopping, planning, and general guidance.
6. Persistence
History and saved recipes
Useful outputs are stored for continuity, personalization, and follow-up work.
Principles
Product rules that shape the interface.
Speed first
Simple tasks should feel immediate. If a question can be answered locally, it should not wait on the network.
Multimodal by default
Typing, speaking, and image input are part of the same workflow instead of separate tools.
No content bloat
No forced reading, no engagement bait, and no extra narrative before the useful answer.
Transparent AI boundaries
The system should make it clear when an answer is instant and local versus slower and cloud-dependent.
Team
Who is building it.
Systems
Yunus Emre Vurgun
Architecture, machine learning integration, infrastructure, networking, and security.
Backend and data
Yusuf Ceylan
Data pipelines, analysis systems, and backend development with a strong operational mindset.
Contact
Need to reach the team?
Use the contact page for product questions, feedback, and partnership inquiries.