AI Venue Booking

Flaash is an AI-driven venue booking marketplace that simplifies finding the ideal venue for corporate events. Its intelligent assistant helps event planners discover venues tailored to their preferences, enhancing the search and booking experience.

Key features

• AI-powered search: matches venues by style, capacity, and proximity using vector embeddings.
• Personalized recommendations: curated suggestions that explain why each venue fits the brief.
• Seamless booking: built-in messaging, event briefs, and booking tools for effortless transactions.
• Interactive map search: geolocation filtering on a fast, custom map experience.

A full-code platform built to scale

Flaash started as a low-code MVP to validate the market. Once traction was proven, Lowcodr rebuilt it as a custom web application — a modern Next.js front end backed by Supabase (PostgreSQL), AI vector search, and an LLM-powered assistant — migrating the full venue database of 17K+ listings in the process. The result: faster search, 70% lower software costs, and a codebase ready for the next stage of growth.

The platform is live at flaash.co.

Creating Flaash presented several technical and operational challenges that required innovative solutions:

1. Combining AI and user preferences

Developing a search engine that effectively combines vector-based AI search with geolocation data and user preferences was a complex task. The system needed to accurately match venues by style, capacity, and location while maintaining fast query times.

2. Personalized recommendations at scale

Implementing a recommendation system that explains why a venue suits the user’s needs involved leveraging advanced AI models and integrating them seamlessly into the platform. Balancing personalized insights with a smooth user experience was critical.

3. Real-time search efficiency

The marketplace required a highly responsive search system capable of handling large datasets of venue details without compromising performance.

4. Migrating from MVP to a custom stack

Moving from the validation-stage MVP to a full-code platform meant migrating 17K+ venues, photos, and structured data into a new PostgreSQL database without breaking the live product — while cutting software costs by up to 70% through a rebuilt data layer and search pipeline.

5. User-friendly booking flow

Ensuring a seamless booking experience with integrated messaging and transaction tools required building workflows that handled complex user interactions while maintaining simplicity and reliability.

These challenges were addressed with a combination of advanced AI tooling, precise geolocation techniques, and performance-focused engineering, resulting in a robust, scalable, and user-friendly venue booking marketplace.