Biomedical Knowledge Graphs & Graph-RAG Systems
Unify literature, omics, structural biology, assays, and internal datasets into one scientific knowledge engine – powered by graph reasoning, embeddings, and agentic RAG.
Accelerate scientific discovery with AI-powered knowledge graphs and Graph-RAG systems
Learn moreAgentic AI for Hypothesis Generation
Multi-agent reasoning pipelines propose mechanistic hypotheses, biomarkers, MoA candidates, or target–disease links.
Biomedical Knowledge Graph Construction
Integrate and harmonize data from scientific literature, patents, omics repositories, protein databases, pathways, clinical datasets, and your internal research systems.
Graph-RAG Systems for Scientific Question Answering
LLM-powered retrieval augmented with biological embeddings, entity disambiguation, and graph traversal.
Literature Mining & Automated Evidence Mapping
Continuous ingestion and annotation of new publications, preprints, patents, and datasets.
Multi-Omics Integration via Graph Reasoning
Connect bulk RNA, scRNA-seq, proteomics, metabolomics, structural data, and phenotypic screens.
Enterprise Deployment (HIPAA / GDPR / PHI compliant)
Secure, on-premise or VPC deployment with full audit trails, access control, API integration, and wet-lab platform connectivity.
Key
R&D Challenges We Address
From fragmented data to unified scientific context
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Teams waste hours switching between papers, databases, ELNs, and internal notes
Knowledge graphs consolidate everything into a single reasoning layer
Reducing hallucinations & ensuring traceability of LLM outputs
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Standard LLMs provide fluent but untrustworthy answers
Graph-RAG ensures every answer is backed by real scientific evidence
Accelerating early-stage hypothesis generation
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Discovery cycles slow down due to manual literature reviews and scattered datasets
AI agents propose and validate hypotheses 10× faster.
Connecting internal and external R&D knowledge
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Data silos between teams cause duplicated experiments and missed insights
Graph-based retrieval provides cross-team visibility and knowledge reuse
De-risking IND-enabling work
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Predicting immunogenic and liability-prone sequence regions before preclinical studies
Scaling research without scaling human labor
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As pipelines grow, manually curating and reading data becomes impossible
Continuous ingestion + automated evidence mapping = scalable research ops
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Tell us what you’re building – we’ll map out the options, evaluate feasibility, and recommend the optimal technical pathway.
Get a consultationBiotech
Industry
USA
Location
AI Ops & MLOps, Cloud Architecture, UI/UX Design
Services
$200,000 to $999,999
Budget
The team surpassed expectations on timelines, provided much needed guidance and overall input on design, all while operating with a high degree of autonomy.
Carl Kaub
Vice President of Chemistry at HTG Molecular DiagnosticBusiness Impact You Can Expect
Book Strategy Call5–10×
Faster Literature & Evidence SynthesisGraph-aligned retrieval drastically reduces time spent on manual review.
70–90%
Reduction in Missed InsightsKnowledge graphs reveal non-obvious mechanistic links missed by keyword search.
2–4×
Faster Hypothesis GenerationAgentic pipelines accelerate exploration of targets, biomarkers, combinations, or MoA.
>95%
Answer GroundednessGraph-RAG ensures answers always reference real evidence — no hallucinations.
Let’s build your AI advantage
Whether you’re prototyping a molecule scoring system or looking to automate your clinical ops – we’ll help you turn your biotech data into competitive edge.
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