How Allref Works

Life sciences research data lives across independent databases, each with its own schema, identifiers, and gaps. The platform connects them into a single knowledge graph where researchers, publications, products, organizations, trials, patents, and grants are all linked.

Public databases are indexed out of the box. Private and proprietary datasets join the same graph through the same integration pipeline.

The same entity appears across multiple databases under different IDs and name formats: a researcher in PubMed, a PI in NIH Reporter, an inventor in USPTO. An organization listed one way in a trial registry and another way in a grant database. The entity resolution engine links them into unified records: researchers, organizations, and products connected across every source.

Standard search tools treat each document as an isolated block of text. Allref's Language Graph Model learns from the connections between records: who cites whom, where work is published, which entities co-occur across datasets. Records connected by shared research communities surface as related, even when their text differs. The model is extensible to any structured or semi-structured data source.

This powers similar-researcher discovery (find people like your best customers), personalized recommendations, and predictive landscape clustering.

Researchers cite specific instruments, reagents, and kits in their publications for reproducibility. The platform extracts those mentions and attributes them to the correct company, even when the same product name maps to multiple vendors.

A product name like "NovaSeq" resolves to Illumina automatically. A generic term like "PCR kit" requires a nearby company mention for attribution. This is how the platform turns unstructured text into structured commercial data.

Seven entity types are searchable from a single interface: researchers, publications, products, patents, clinical trials, organizations, and product users. Filter by geography, institution, date range, and research area.

Every search result links to related entities across the graph. A researcher result shows their publications, product usage, grants, and collaborators. A product result shows who cites it, where, and in what applications.

Type a question in plain language. Allref's Alluna AI agent with 40+ specialized tools determines whether to search for researchers, run a competitive landscape, build a persona, pull product usage data, or generate a research map. Multi-step questions work too.

Every claim traces back to a specific publication, trial registration, or patent filing. Click through to the source.

Describe a market. The platform identifies the relevant companies, products, and topics, then pulls citation data month by month.

The output is a structured analysis: market share trends by company and product, workflow analysis showing how products are used together, competitive differentiators at the application level, and an adoption outlook based on where usage is heading. All built from what researchers actually cite, not surveys.

Buyer personas are built from real researchers, clinicians, and PIs, scored and connected by the Language Graph Model: what products they cite, what grants fund their work, their career stage and influence level, and their collaboration patterns. Use them to identify target buyers for existing products and inform new product development.

The output includes purchasing factors (performance, cost, compatibility, innovation appetite, brand loyalty) and specific marketing recommendations. Not fictional archetypes built from interviews. Specific enough to inform messaging, non-identifying enough to use freely.