Bohrium LKM (Large Knowledge Model)v1.0.0

Researchers can use the knowledge graph search to quickly find relevant scientific findings and understand relationships between variables in their field. This reduces time spent on manual literature review and helps identify gaps or supporting evidence for new research.

Journalists, policy makers, and fact-checkers can verify the accuracy of scientific claims by using the claim matching endpoint. This ensures that public statements are backed by reliable evidence and helps combat misinformation.

R&D teams in industries like pharmaceuticals, materials science, and energy can use variable relationship queries to guide experimental design. For example, understanding how temperature affects catalytic activity can help optimize industrial processes.

Organizations with large paper repositories can use the batch OCR capability to convert unstructured paper content into structured, searchable data. This facilitates building knowledge bases or feeding into machine learning pipelines.

Researchers can strengthen grant proposals by demonstrating the novelty and evidence base of their proposed work. The evidence chain retrieval can help cite specific papers and evidence that support or refute existing claims, highlighting research gaps.

Charge researchers, companies, or academic institutions a recurring fee for API access, with tiered pricing based on usage levels (e.g., number of requests, batch sizes). This provides predictable revenue and supports ongoing maintenance and updates.

Charge per API call or per unit of data processed (e.g., per OCR page, per claim match). This model is attractive for users with occasional or variable demand, allowing them to scale costs with usage.

Offer basic access (e.g., limited searches, limited OCR) for free, while charging for advanced features like batch processing, higher rate limits, access to full evidence chains, or priority support. This can attract a wide user base and upsell to heavy users.