Books Shouldn’t Be Hard to Find in Your Library!
28, April 2025
Infiniti AI is transforming tagging and searching
Finding the right book in a school library catalogue shouldn’t be a struggle. Yet, in many schools, students walk away frustrated – not because the library lacks resources, but because they couldn’t find them.
It’s not a collection problem. It’s a metadata problem.
Concord Infiniti’s AI Metadata system was built to fix that, transforming how search and cataloguing work in school libraries. But to understand how powerful it is, we need to look at how most school libraries operate.
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:
- Title
- Author
- Series
- Subject
- Abstracts
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:
“No results found.”
“Check your spelling.”
“Choose a word from this list.”
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:
Colloquial terms (e.g., “dinos” for “dinosaurs”)
Common misspellings
Localised language (e.g., “aussie rules” for “Australian Rules Football”)
Non-English terms used in multilingual communities
But here’s the catch:
Every tag must be applied to every individual record.
Every unique title must be manually updated.
This process is time-consuming and prone to errors.
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.
AI Metadata Editor
Lets library managers train Infiniti to recognise subject-orientated words and phrases – alongside misspellings, colloquialisms and non-English terms.
Just tag once, and it applies to all past and future resources with that attribute!
AI Metadata Search Assistant
Sits silently behind the Infiniti OPAC and interprets student search intent and retrieves relevant books, even if there’s no exact match.
No more student frustration – just consistent, intuitive and helpful results!
Why it matters
For students, Infiniti AI means:
- Easier access to resources
- Fewer dead-end searches
- A more engaging experience and confidence in the library system
For librarians, Infiniti AI means:
- Less time spent on data entry
- Always discoverable resources
- A future-proof system that updates as your collection grows
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.