AI-powered OCR extracts every article, adds historical context, and makes centuries-old newspapers fully searchable — including by meaning, not just keywords.
We're onboarding researchers, archivists, and institutions one at a time. Approval typically takes 1–3 business days.
A short walkthrough of extraction, semantic search, and the research tools.
Three simple steps to transform scanned documents into a searchable, AI-enriched archive.
Drag and drop scanned pages (JPEG, PNG, TIFF, PDF), or paste a URL to import from an online repository.
Our multi-stage AI pipeline analyzes layout, transcribes columns and advertisements including rotated edge content, structures the result into articles, and re-checks the extraction against the original scan to find and repair gaps.
Browse your digitized library, search by keyword or meaning, ask questions with the AI Librarian, and explore AI-generated historical context for every page.
Everything you need to digitize, search, and analyze historical documents.
Reads 5-7 column layouts, rotated ads, tables, and edge content from historical broadsheets.
Automatically separates and classifies articles, advertisements, legal notices, and mastheads.
Generates historical context and era-relevant annotations for each extracted article.
Search by meaning, not just keywords. Vector-powered search finds relevant articles even without exact word matches.
Ask questions across your entire archive in natural language and get AI-powered answers with source citations.
Paste a link to a PDF or page image — including archive.org and institutional repositories — and the platform fetches and processes it.
Searchable PDF, ALTO/XML, JSON, and Markdown exports for integration with library systems and research tools.
Auto-typed content: article, advertisement, legal notice, public announcement, masthead, and more.
Researchers, archivists, and institutions use ArchiveLM to turn historical collections into searchable, analyzable knowledge bases.
Layout-aware OCR that reads historical broadsheets as they were typeset — column by column, ad by ad — and makes every article semantically searchable.
Learn morePurpose-built pipeline for Hansard and legislative records — extracts speaker-attributed debates, committee proceedings, and legislative journals into a fully searchable, citable corpus.
Learn moreOCR and semantic search platform built on Spanish-language Latin American primary sources — colonial-era typography, 19th-century broadsheets, and archaic orthography handled natively.
Learn moreOCR pipeline optimized for the linear, single-column structure of historical books and manuscripts — from 16th-century printed books to 19th-century institutional records — with semantic search over the full text.
Learn moreStructured AI extraction for historical legal records — court proceedings, land grants, edictos judiciales, probate records, and registry documents — searchable by case, party, date, and concept.
Learn morePurpose-built digitization for denominational archives — parish registers, diocesan records, missionary correspondence, and institutional histories — made searchable for researchers, genealogists, and faith communities.
Learn moreA complete digitization and research platform for university special collections — from OCR and semantic search to AI-generated research tools and researcher workspaces.
Learn moreTurn decades of morgue files, clipping envelopes, and back-issue archives into a searchable investigative research tool — find historical precedents, track story threads, and surface context that changes today's coverage.
Learn moreCompiled from publicly available competitor documentation. Competitor feature sets change — verify current capabilities directly before relying on this table.
| Capability | ArchiveLM | Veridian | Generic OCR | Manual |
|---|---|---|---|---|
| AI Enrichments | Yes | Not documented | No | No |
| Semantic Search | Yes | Not documented | No | No |
| RAG Chat | Yes | Not documented | No | No |
| Article Segmentation | AI-powered | Manual + AI | No | Manual |
| Processing Speed | 1-3 min/page | Hours | Seconds (OCR only) | Much slower |
| Historical Expertise | Native | Yes | Generic | Depends |
Sources: published product documentation for Veridian (veridiansoftware.com), Google Document AI and Amazon Textract. "Not documented" means the capability is not described in that vendor's public materials, not that it is absent.
We're working closely with researchers, archivists, and institutions during this private beta. Tell us about your collection and we'll be in touch within a few business days.
Approved beta accounts get hands-on support during onboarding so you can validate results on your own documents.