AI innovation
Native integration with artificial intelligence for advanced automation of cataloging, metadata enrichment and predictive analysis.
Built-in AI · 23+ years of experience
Advanced technology for 21st-century information management
AI-based comprehensive systems for libraries, archives and digital repositories. More than 1,000 institutions trust our innovative solutions.
1,000+institutions
23+years of experience
5countries with local teams
24/7technical support
JANIUM LEADS INNOVATION
Cutting-edge technologies that transform institutional information management
Total flexibility
On-premise or SaaS: deployment tailored to your needs. Automatic scalability and seamless updates.
Global standards
Compliance with MARC21, Dublin Core, ISAD(G), OAI-PMH and more. Guaranteed interoperability with international ecosystems.
24/7 support
Expert technical service available whenever you need it. A global team of specialists across 5 countries.
PRODUCTS
Every product designed to maximize your institution's potential
Core system
Janium
Comprehensive management system for libraries, archives and digital repositories. Three specialized applications on a unified platform with integrated AI.
Learn more → Premium SaaSJaniumNet
Your Janium system in the cloud with enterprise-grade infrastructure. Eliminate CAPEX, optimize OPEX.
Learn more → AI + DigitizationJaniumCollect
Automated capture powered by LLM. Intelligent metadata extraction with no predefined templates.
Learn more → Business IntelligenceJaniumBI
Turn Big Data into strategic decisions. More than 80 preconfigured dashboards.
Learn more →
CLIENTS
Institutions that trust Janium
FROM THE BLOG
Latest posts
Why trust a catalog made with AI
How a catalog made with AI becomes trustworthy: not by denying that the model enriches, but by knowing where each piece of data comes from. It rests on the source that backs each one, verification against authorities, deterministic computation of rule-based fields, per-record evaluation and a quality gate that stops degraded batches, with the limits made explicit.
How to describe an archive in ISAD-G with AI
What sets archival description apart from bibliographic description, why an archive needs ISAD-G, and how ISAD-G description is produced with AI —by provenance and hierarchy, not item by isolated item—.
Glossary: the AI and cataloging terms in this blog
Short definitions of the terms that cross this series: from AI (language model or LLM, frontier model, on-premise, air-gapped, anonymization, OCR, ASR) and from cataloging (MARC21, Dublin Core, ISAD-G, CDWA, authority control). For readers coming from libraries and archives who run into AI jargon, or the other way around.
Cataloging with AI without the material leaving the institution
Some collections cannot leave the institution — data residency, policy or confidentiality. How Collect catalogs with AI on the institution's own infrastructure, with a local model and even air-gapped; the hybrid mode with anonymization for the hard cases; and why the rare thing isn't sovereign AI or cataloging on their own, but the two together. With the limits of each mode.Ready to modernize your institution's information management?
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