Traditional catalogues:
Built for standards, not students

Most Australian school libraries use catalogues built by importing Z39.50 bibliographic (MARC) records from SCIS into their library management systems. These records contain highly structured, standards-based attributes, including:

While consistent and technically sound, these records use formal and controlled vocabulary – which rarely reflects the language students actually use when searching.

The search disconnect

Consider a student looking for books on dinosaurs. The SCIS subject might be “prehistoric animals”, but a student is more likely to search for “dinosaurs”, “t-rex”, “raptors” or even misspellings like “dinasores”.

The same goes for “tsunamis” vs. “tidal waves”, or “World War II” vs. “Hitler” or “D-Day”. Even though the books are in the library, students are likely to receive a response like these from their searches:

This is because the search algorithm in most library management systems can’t interpret what the student meant. They rely on an exact match with a limited set of controlled fields.

Manual tagging

To combat this, most library management systems allow librarians to add relevant tags or keywords to bibliographic records. These might include:

But here’s the catch:

For example, tagging 50 books about ancient Egypt with “pharaohs”, “mummies” and “Cleopatra” means editing all 50 records – one by one. Forget to tag just one? It’s invisible in search.

The Infiniti AI alternative:
A smarter and faster system

Infiniti is different. Instead of tagging individual bibliographic records, it tags metadata attributes. It’s a smarter, faster and far more scalable way to manage discoverability.

Why it matters

Don’t spend 30 seconds adding a single tag to every relevant bibliographic record. With Infiniti AI, it takes 10 seconds to train one metadata element for each relevant subject.

✔️ 10 seconds to train for 20 books
❌ 10 minutes to tag 20 books

Want to see Infiniti AI in action?
Talk to the team at Concord for a demo.