Institutional memory deserves modern software.
Most archive software is a decade behind. It treats AI as a bolt-on, ignores the track-changes curators actually need, and charges like enterprise while shipping like freeware. We're fixing that.
Our story
Archively.AI grew out of years of work with libraries, university archives, and heritage institutions. The same problems kept coming up: OCR that never quite worked, cataloging software that forced a single workflow, and export formats that pretended standards compliance meant a CSV dump.
We started with the hardest part: a standards-first data model and a multi-tenant architecture that could host a city archive and a specialist research library side by side, without cross-contamination. Around that we built an AI pipeline that drafts instead of decides, and a UI that treats curators as the experts they are.
The platform you see today is v2. The core is stable; the roadmap is ambitious. If you're running an archive and you've given up on finding software that doesn't fight you, we'd love to talk.
What we believe.
Four principles we wrote down early and have stuck to since.
Curators, not algorithms, decide.
AI drafts. People approve. We build tools that keep expert judgement at the center — not vague black boxes.
Standards over lock-in.
Your data round-trips through ISAD(G), Dublin Core, and MARC. If we disappeared tomorrow, you'd still have everything.
Boring infrastructure is a feature.
Archives run for decades. We pick dependencies, storage formats, and deployment models with that horizon in mind.
Make the UI fast.
Collections grow; interfaces shouldn't slow down. We profile, measure, and fix — not ship and forget.
Want to compare notes?
Whether you're evaluating tools or just curious what good archive software looks like in 2026 — drop us a line.