AI knowledge layer for scientific publishers
ScholarKnowledge.ai
Activating the Intelligence within scholarly content
Transform decades of peer-reviewed research into a queryable knowledge graph — delivered through modern AI access models: RAG, MCP, A2A, and metered APIs.
Trusted content
AI-grounded insights drawn from peer-reviewed scholarly archives — every answer anchored to a real source.
Knowledge graph
Vector embeddings, entities, and citation links woven into a living, continuously updated knowledge layer.
Intelligent access
Natural-language queries via RAG, plus MCP, A2A, and REST endpoints ready for agent workflows.
Built for publishers
A monetizable AI surface for your archive — token-metered, transparent, and fully attributed.
The challenge publishers face
Decades of scholarship — locked inside PDFs.
Locked-away knowledge
Millions of articles sit in PDFs and databases — accessible but not queryable. Cross-article insights take hours to extract.
Discovery friction
Keyword search misses semantic connections. Researchers wade through irrelevant results to find what matters.
Untapped monetization
Your content has enormous value beyond subscriptions — but no infrastructure exists to deliver it programmatically.
No AI-ready interface
Enterprise customers and AI agents need structured, metered access to scientific content. Most publishers can't provide it.
Introducing ScholarKnowledge
A full-stack platform that turns your archive into a living knowledge graph.
Ingest & extract
- — Structural parsing of PDFs, XML, HTML
- — Semantic entity extraction
- — Citation graph construction
- — Metadata enrichment
Knowledge graph
- — Vector embeddings per chunk and concept
- — Taxonomy and ontology mapping
- — Cross-article relationship linking
- — Continuously updated index
Intelligent access
- — RAG-powered natural-language queries
- — MCP, A2A, and REST endpoints
- — Token-metered consumption billing
- — Cited links back to source articles
Powered by retrieval-augmented generation
Researchers ask. Your archive answers — with citations.
Query
"What are the latest findings on mRNA vaccine efficacy against novel coronavirus variants?"
AI-generated response
Based on 47 relevant articles in your archive, studies published in 2023–2025 show mRNA vaccines retain 78–91% efficacy against Omicron sub-variants when boosted within six months. Three landmark trials demonstrate significant T-cell response preservation even against BA.2.86…
Novak et al., Nature Medicine 2024 · Chen & Williams, Lancet 2025 · RECOVERY Collaborative, NEJM 2024 · +44 more
How it works
The ingestion pipeline.
- 01
Content ingestion
Feed raw content — PDFs, XML, HTML — via secure upload or continuous sync connector.
- 02
Structural extraction
Parser identifies sections, figures, tables, references, equations, and metadata.
- 03
Semantic extraction
NLP pipeline extracts entities, concepts, claims, and relationships — a rich semantic layer.
- 04
Vector embedding
Each chunk is embedded into a high-dimensional space using state-of-the-art scientific language models.
- 05
Knowledge layer population
Embeddings, entities, and metadata are indexed into the vector database with citation links preserved.
Why ScholarKnowledge
Faster literature discovery than keyword search
Source attribution on every AI-generated answer
Access protocols: RAG, REST, MCP, A2A
Scales with your archive
Revenue stream beyond subscriptions
Ready for autonomous agent workflows
Ready to unlock your archive?
Power the next generation of AI-driven research workflows.
Schedule a pilot with your content, explore custom integrations, or join our Publisher Partner Program.