Solutions
Turning research into
instruments for real impact.
Medical research, AI, system development, and real-world implementation.
By bridging these four domains, we transform your research into usable form.
Four disciplines — Drug Discovery, Data, Evidence, and Infrastructure.
Drug Discovery Support
Find the next move in drug discovery,
from within your data.
3.2M compounds › 12,847 screened › 17 candidates › 1 target From your compound library to a validated target.
Problem
You have vast experimental data, compounds, and literature — but the next target to pursue and the promising hypotheses stay buried. You need to analyze your competitive drug-discovery IP without exposing it to the outside.
Approach
We combine medical-research knowledge with AI analysis to derive new hypotheses about targets, mechanisms, and candidate compounds from your drug-discovery data. We cross-read literature, patents, and in-house experimental data, surfacing relationships that were overlooked. Because everything runs on-premise, your unpublished research IP is protected throughout.
Capabilities
Hypothesis generation & prioritization for targets and mechanisms
Cross-integration of insights across compounds, papers, and patents
Multi-omics and molecular data integration analysis
Fully on-premise processing that keeps research IP inside
Process
Bring your data
Experiments, compounds,
literature, omics
Integrated analysis
AI extracts relationships
and patterns
Derive hypotheses
Prioritized candidates
for targets & mechanisms
Feed into validation
Next experiments &
joint research
Focus
Data Analysis
Turn dormant data
into meaningful results.
CT · MRI · Omics · Clinical › Explainable AI Analyses tailored to your questions, with reasoning you can explain.
Problem
You hold precious clinical data, images, and omics — but the methods, hands, and time to analyze them are lacking, leaving their value unrealized. Entrusting them to the cloud via an external institution is difficult from a confidentiality standpoint.
Approach
We analyze medical imaging (CT, MRI, pathology), omics, clinical, and behavioral data with machine learning. Using physician diagnoses and experimental outcomes as supervision signals, we go beyond aggregation — we design analyses whose reasoning can be explained. Whatever data, however you want it analyzed — we customize the analytical approach itself to match your questions.
Capabilities
Medical image AI analysis (CT, MRI, pathology)
Multi-omics and genomics analysis
Multimodal data integration and decision-making
Explainable analytical model construction
Data Types
Medical Images
CT, MRI, pathology
microscopy
Omics
Genome
transcriptome, more
Clinical & Behavioral
Test values, voice
expression, vitals
Documents & Records
Charts, lab notes
reports
Focus
Manuscripting
Turn your findings into
globally cited evidence.
10+ papers › 16.6 max IF › 3 fields End-to-end evidence-building, under distinguished university professor supervision.
Problem
Even with excellent data or products, turning them into peer-reviewed papers — the world's shared evidence — requires specialized writing skill, academic authority, and time.
Approach
OmiCore handles everything from experiment and analysis through data interpretation, drafting, and submission — end-to-end, under supervision of a distinguished university professor. You can start from just a research theme, from unanalyzed data, or from complete results. We accompany you through target-journal selection and reviewer response, aligned to your target impact factor.
Capabilities
From a theme, end-to-end — experiment design through publication
From data, to results — turning unanalyzed data into papers
From results, to a paper — writing, figures, submission support
Target-journal strategy & peer-review response by IF tier
IP protection — organizing findings toward patent filing
Strengthening products with evidence — functional claims backed academically
Process
Theme / Data intake
Locate starting point
Scope alignment
Experiment & Analysis
Cells, mice, C. elegans
Data analysis
Writing & Supervision
Distinguished university
professor supervision
Submit & Respond
Journal selection
Response to reviewers
Focus
Local AI Foundation
The foundation beneath it all —
where data never leaves.
Air-gap › Llama · Mistral · Qwen › RAG A fully local AI foundation — patient, drug-discovery, and research data never leaves.
Problem
The more confidential your data — patient records, drug-discovery IP, unpublished research — the harder it is to place on cloud AI. So you get left behind by the benefits of generative AI. We want to resolve this contradiction at its root.
Approach
We run all three solutions above on a fully local AI stack contained within your facility. External transmission is not a "policy" — it doesn't exist as an architecture. Open-source LLMs (Llama, Mistral, Qwen) and RAG deployed in an air-gapped environment. Scalable from a single-researcher workstation to hospital-scale GPU clusters.
Capabilities
Zero external transmission — safety as architecture
Open-source LLMs + RAG, fully local operation
Configuration compliant with APPI, HIPAA, GDPR
Scalable from individuals to hospital-scale deployments
Process
Requirements
Data types & scale
Compliance
Configuration Design
Hardware
LLM & RAG selection
Local Deployment
Deployed in
air-gapped environment
Operational Support
Updates
Tuning
Focus
Contact
Try first, decide after.
No large commitment required at the start.
Bring your data and the questions you want answered — we'll prove value first, then move to the next step. NDA-friendly.