
Intelligent Metadata, Consistent Descriptions, and Search Optimization for Media Archives.
ARCHIVAL AI by


Generate
Auto-tags, transcripts & OCR from video, image, audio, text
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Schema-aligned fields (CDWA, CCO, Dublin Core or custom)
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Content clustering to reveal emergent categories
Govern
Consistency & quality you can trust.
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Rules engine enforces tone, terminology & placement
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Ontologies/authority files applied across the archive
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Automated evaluation (precision/recall) vs. human baseline
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Audit trail and versioned outputs for review

Discover
Search that understands your collection.
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Semantic + keyword search across all content types
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“More like this” via vector similarity clustering
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Standardized searchability even with uneven metadata quality
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Curator-friendly facets and filters
The Story of Archival AI
Drowning in files—or discovering what matters?
Archival AI by onto turns raw media into structured, searchable records: it extracts tags, transcripts and on-screen text from video, image, audio and documents, and maps them to your chosen schema. It enforces consistency and powers semantic + keyword search with similarity clustering—so one can actually find what they need.
Industries We Serve
Marketing & Sales Enablement
Reuse marketing and training media—and make it instantly sales-ready. Search by campaign, product, segment, region, speaker or rights; enforce approved & latest versions and cut duplicate shoots.
Cultural Heritage & Artist Estates
Keep heritage discoverable. Archival AI drafts schema-aligned records (CDWA/CCO/DC), applies authority files, enforces terminology, and clusters related works—experts review and approve.
Media & Publishing
Find the right clip in seconds—index speech, on-screen text, visuals and music; filter by transcript status, language, rights or topic; jump to timecodes.
Universities & Research Labs
Make lectures and research media truly searchable—ASR transcripts, OCR, topic facets and named-entity linking for citations.
Brand & Enterprise
Make your company’s history usable. Search past events, launches and talks; copy time-coded moments and visuals straight into decks and wikis. No more hunting across drives.
Our
Solutions

Strategy
We inventory your media, systems, and policies, map sources and risks, and align on goals. If holdings are still physical (tapes/film/photos), we scope digitization & ingestion as part of the plan.
Execution
Co-design your data model (CDWA/CCO/DC or custom), authority files and rules; define review/QA workflows and KPIs—human-in-the-loop by default.

Data Advisory
We deploy Archival AI, centralize and index all your media, generate drafts and dashboards, and train your team—handing over a searchable, governed archive.
Previous Work
Our relevant work that have been completed with partners from various industries.

Nokia Design Archive
Nokia Design Archive is a publicly accessible digital portal developed as a research project at Aalto University’s Department of Design, built from materials donated by Microsoft Mobile Oy and Nokia designers to study the role of design within big organizations like Nokia. The source archive comprises 20,000+ entries and about 950GB of files spanning from the mid-1990s to 2017. Lu Chen as a member of the research project began prototyping interactive visualizations in 2023, continued the web application development in the summer of 2024. The portal was launched in January 2025 with support from Aalto University Communications. This project “remediates” archival material into accessible knowledge through interactive visualizations for a non-academic audience.
Nokia Design Archive &
Aalto University

Teoman Madra Archive
Teoman Madra Archive is an ongoing conservation and digitization effort launched in 2021 to rescue and organize the multimedia oeuvre of Teoman Madra, a pioneer of Turkish media art, focusing on works produced between the 1960s and 2000s. The team (Begüm Çelik & Selçuk Artut) first retrieved materials scattered across locations, stabilized fragile carriers, and began systematic digitization and cataloging of slides, negatives, VHS/Betamax/miniDV tapes, optical media, and drives. Descriptions follow museum-grade standards (CDWA/CCO) and include both Madra’s artworks and historically valuable documentary recordings, building a research-ready corpus.
Sabancı University &
Madra Family

Tuumailubotti
Tuumailubotti is an experimental conversational AI that explores how a chatbot might represent neurodiverse rhetoric rather than defaulting to neuronormative styles. Built on Finnish FinGPT-3 models to achieve native-level Finnish proficiency, it was evaluated with 31 participants, and its curated dataset has been released openly for further research. In short, Tuumailubotti blends HR support, design research and local-language AI to question how conversational systems can better include neurodivergent ways of communicating.
Finnish Broadcasting Company