Archive as a Working Resource

Client challenge

Over more than twenty years of work, the client had accumulated a large text archive: tens of thousands of materials, related publications, dates, authors, sections, service attributes, and other important data.

The materials were created and stored at different times using different technical solutions: old databases, interfaces, and sets of attributes. Later, this data ended up in fragmented local backups, which made it difficult to search, check archive completeness, move materials into new interfaces, and continue working with them.

The goal was to extract and preserve texts without losing significant information, restore the archive structure, separate useful content from outdated technical elements and heavy media content, and prepare the data for modern search, grouping, new interfaces, and further work using AI.

Industry / business type

Media and journalism. In such archives, value lies not only in the texts themselves, but also in the context around them: publication date, authorship, section, thematic links, source, service tags, and the ability to quickly find the right material years later.

Implemented solution

Automated processing of local backups and old HTML markup was carried out. A new database structure was developed for the project, combining different legacy ways of storing materials and bringing the archive into a unified logic.

Article texts, titles, dates, sections, links between materials, and related metadata were extracted from the archive files. The content was cleaned of outdated system information, technical artifacts, and unnecessary media files that were no longer required for further work with the archive.

After processing, the materials were reorganized into a unified structure in a modern database. Fast full-text search across the entire archive was added, and a foundation was prepared for future interfaces, filters, thematic collections, and more precise semantic search.

A separate layer was created for external AI tools to work with the archive. This allows the materials to be used not only for ordinary keyword search, but also for further analysis, grouping, selection, and preparation of answers to more complex queries.

Technologies used

Special scripts were created for the project to parse old markup, separate useful text and metadata from the technical environment, and check the result for integrity.

SQLite was used as the storage layer — a compact database that does not require a separate server environment to be maintained. For search, a built-in full-text index was connected, allowing fast searches by words and phrases across the entire archive.

An additional data structure was prepared for connecting external AI tools and further developing semantic search.

Business result

A multi-year archive was preserved and moved from outdated infrastructure into a reliable modern structure. The company no longer depends on several old technical environments that are difficult to maintain, develop, and operate safely.

Materials can be quickly searched, filtered by attributes, grouped, prepared for publication in new interfaces, and used as a foundation for further analysis. The archive became not a set of old files, but a working information resource suitable for developing new products, editorial tools, and intelligent search.