Context
The challenge
Restaurant discovery platforms need to combine rich data, search, and recommendations at scale. Traditional approaches lack intelligent understanding of user intent, cuisine context, and real-time availability. Delivering relevant, personalized discovery across large catalogs is challenging without AI-powered search and multi-agent reasoning.
How we worked
Our approach
We designed and built an AI-driven restaurant discovery system for Miso AI using multi-agent architecture and RAG. The system combines AI with real product engineering to deliver powerful discovery and recommendation experiences.
Delivery
The solution
The platform uses multi-agent AI to reason about user preferences, cuisine types, and context. RAG enables accurate search over restaurant data, menus, and reviews. Intelligent recommendations and discovery flows help users find and explore restaurants with minimal friction. Built for scale and real-world product deployment.
Results
Key metrics
- Multi-agent
- Architecture
- RAG-powered
- Search
- AI-driven
- Recommendations
- Production-ready
- Scale
Impact
Results & outcomes
- Intelligent restaurant discovery powered by multi-agent AI and RAG
- Accurate, context-aware search over menus and reviews
- Personalized recommendations improving user engagement
- Scalable architecture for large restaurant catalogs
- Strong combination of AI and product engineering for production
Tech used
Technology stack
Tools and patterns from this engagement—your stack may differ.