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Galactic AI™

From biomedical literature to causal, directional, source-traced evidence

Galactic AI is Biorelate’s LLM-native curation engine that identifies, curates and structures biomedical entities and the relationships between them.

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Unstructured text Gene AincreasesDisease Y Gene A increases Disease Y Galactic AI Structured relationship subjectrelationshipobjectsource Gene A increases (+) → Disease Y ontology IDtyped · signedontology ID sentencedocument ID

How Galactic AI works

Three steps take the literature from unstructured text to structured, FAIR cause-and-effect data in your own stack.

  • Expert Manual Curation

    Fine-tuned LLM models for NER and RE trained on 10+ years of curated data & insights.

  • Science-Driven Quality Control

    Data compared to human curated gold standard. Full model performance benchmarks available on request.

  • Direct Links to Source Documents

    Every relationship links back to the original literature, providing the explainability that regulatory and scientific teams demand.

  • Cross-Sentence Discovery

    Cross-sentence discovery yielding 3x more relationships vs sentence-bound NLP.

  • Instant Ontology Updates

    Instant ontology updates to expand your research space without re-processing the entire corpus.

  1. Unstructured documents

    Galactic AI has processed >61M documents from:

    PubMedPMCbioRxivmedRxivUSPTOEPOClinical trialsDrug labelsGrants

  2. Galactic AI technology

    • Fine-tuned LLMs

      NER and cross-sentence RE, directional and signed

    • Structured and FAIR

      8 relationship classes and 23 entity classes linked to 22 ontologies

    • Database-side post-processing

      Instant ontology mapping

  3. Delivery & access

    Data DownloadAPIWeb

Galactic AI

See the evidence behind every causal path

With Galactic AI, every relationship comes out typed, directional, resolved to a public identifier and traceable to the sentence it came from. The example below follows one sentence through curation: the entities it mentions, the relationships between them and the causal path they form. Select any part to see how it connects, or run the trace to watch it build.

One sentence, traced into the graphExample biology, for illustration
Sentence input

decreases kinase activity and is used to treat .

Entities 3 found
Relationships Galactic AI
Causal path 3 hops

Select any part of the example to see how it connects, or run the trace.

Coverage

  • >61Mdocuments
  • 358Mlinked mentions across the literature
  • 5.3Bentity mentions (3.5B linked)

Structure

  • 55.7Munique linked cause-and-effect relationships
  • 8relationship classes
  • 23entity types
  • 22ontologies linked

Quality

  • 82%RE precision (PubMed)
  • 93%combined NER + linking accuracy

Data compared to human curated gold standard. Full model performance benchmarks available on request.

Galactic AI works with your existing stack

Choose your integration.

  • Galactic Data™

    Relationships and entity annotations delivered as downloadable parquet files to integrate into your own graph, warehouse or ML pipeline.

  • Galactic API and MCP

    REST and MCP access to connect our data to your apps and AI assistants, such as Claude or Copilot. We host and maintain the server, so there’s nothing for you to update.

  • Galactic Web

    A co-scientist workspace to navigate cause-and-effect paths and biomarker search, with cited evidence.

  • Galactic Processing

    Deploy our models inside your own infrastructure to curate your licensed & proprietary content.

Evidence, benchmarks and insights

View the Content Hub →
ROI & Business Impact
Data Science Advances
Galactic AI captures the biology GPT-5 misses
In a controlled benchmark, across four established drug targets, Galactic AI v3 captured up to 185x more biological relationships than GPT-5
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Up to 185x
more biological relationships captured with Galactic AI v3 vs GPT-5
13,667
EGFR relationships captured by Galactic AI v3 that GPT-5 missed entirely
71%
of GPT-5's identified entities failed to reproduce across three repeat queries
Therapeutic Research
72% Reduction in Research Time for Complex Target Biology Queries
Answering "What is the role of KRAS in colorectal cancer?" takes a PhD-level scientist 11-16 hours manually. With Galactic AI, it takes 3 hours and covers all 22,500 relevant papers, not just 15.
Learn more
$1.2M
in research cost savings
10,328
hours of analyst time saved
72%
faster target prioritisation
1,291
research queries resolved via AI assistant
Data Science Advances
From 3-6 Weeks to 15 Minutes: AI-Powered Target Safety Assessments
A purpose-built AI workflow for Clinical Safety & Toxicology teams delivering 99% cost reduction and 95% time reduction per target safety assessment, with citation-accurate, regulatory-grade outputs.
Learn more
99%
reduction in literature mining costs
95%
time savings per review cycle
5-10x
increase in data volume processed
99%
citation accuracy maintained

Pre-built, validated cause-and-effect intelligence, ready for integration.

Freeing teams to focus on analysis and discovery rather than data preparation.

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