What Bonjour.mu is built to do differently
Bonjour.mu is designed to make local discovery in Mauritius more structured, more searchable, and more useful through direct search, Bonjour AI, and clearer business data.
Core features
The main ways the product improves discovery
Product-level search
Search for specific items and services instead of relying only on business names.
Bonjour AI
Ask Bonjour AI for help finding businesses, items, and prices in a more conversational way.
AI document extraction
Menus, price lists, and brochures can be converted into structured entries faster.
Structured filtering
Filter by category, tags, location, and price context with less guesswork.
Business dashboard
Owners can review extracted details, update information, and manage visibility.
Mauritius-first logic
The platform is tuned to local places, local discovery behavior, and MUR-based context.
Ownership and trust
Claimed listings and clearer data control help improve reliability over time.
Comparison
Why not just Google or Facebook?
- Searches pages, not structured products or services
- Results depend heavily on SEO and fragmented websites
- Limited local product filtering
- Business information can be incomplete or outdated
- Posts are chronological rather than structured
- Price, category, and service details are harder to filter
- Useful offers get buried in feeds quickly
- Discovery is less reliable when users know what they want
Bonjour.mu
- Indexes individual products and services
- Supports structured search and filtering
- Built around Mauritius-specific discovery
- Uses AI to turn documents into useful search data
Platform model
A directory that behaves more like structured local search
The point is not only to list businesses. It is to make products, services, and practical business details easier to discover and act on.
Traditional directories list businesses. Bonjour.mu is designed to make what those businesses actually sell easier to find.
Bonjour AI adds a conversational layer on top of search, helping users explore options when they do not know the exact listing to open yet.
AI extraction and structured indexing make search results more specific, comparable, and action-oriented.
That same structure creates room for stronger ranking, recommendations, business tools, and richer analytics later on.
Built to scale
The structure is meant to hold up as the catalog grows
Support more listings without making discovery noisier.
Improve ranking quality as more structured data becomes available.
Create a stronger foundation for recommendations, sponsorships, and merchant tooling.
If you want the practical side next, the getting started and tutorial pages explain how these features show up in the actual product experience.