Project framing
About ArchXAI
ArchXAI is a cross-border project exploring how AI can improve archive access, indexing, search, and related public services. This site is the public web version of the project's benchmarking and technology-comparison work.
Project summary
The common challenge addressed by ArchXAI is improving timely access to archive collections while both archival volumes and public information requests continue to grow. The project objective is to improve public services and archival access through jointly developed AI-based solutions that make cataloguing, indexing, and information request handling faster and more usable across borders.
The project outputs described in the application are an open source AI HTR tool, an open source AI OCR tool, a tool for enhanced cataloguing and indexing, and an AI-assisted toolset for information requests. The beneficiaries are archivists, archive users, researchers, and the broader public.
Consortium
| Partner | Role | Country |
|---|---|---|
| South-Eastern Finland University of Applied Sciences | Lead partner | 🇫🇮 Finland |
| The National Archives of Finland | Project partner | 🇫🇮 Finland |
| The National Archives of Estonia | Project partner | 🇪🇪 Estonia |
| The National Archives of Latvia | Project partner | 🇱🇻 Latvia |
What this site publishes
Internally, the underlying material comes from the project's technology-comparison deliverable. For external readers, the purpose is simpler: we test tools, explain what they are good at, and publish useful conclusions as the evidence becomes solid enough to share.
The current publication emphasizes practical questions:
- Which model families are accurate enough for multilingual archival tasks?
- Which approaches are fast enough for large-scale indexing?
- Which tools are realistic to operate inside institutional archive environments?
- Which solutions are still strong enough only for triage and review support, not for autonomous decisions?
Why it is a living site
AI benchmarking changes quickly. A static report can capture one moment, but it cannot stay useful for long without frequent revisions. This site therefore acts as a living publication that can grow with the project.
That approach matches the open publication logic described in the project application. Developed tools, codebases, and AI models are intended to be shared through open platforms such as GitHub and Hugging Face, and this site makes the comparison process visible alongside those outputs.
Where to follow the project
Open publication logic
The project application states that developed tools, codebases, and AI models will be shared through open platforms such as GitHub and Hugging Face. This site supports that commitment by making the comparison process visible in a public and maintainable format. It can be updated whenever new models, benchmarks, datasets, or operational findings emerge, without waiting for a full report revision cycle.