AI models and systems for data-driven scientific discovery.
Models trained on real multi-omic experiments, not just language. Use ours, or build your own.
Trusted by researchers at
All of biology, in one space
Archimedes learns the structure of human biology from real experiments — not text — and places any sample into a single, batch-free space. Data from different platforms, labs, and studies finally becomes directly comparable.
Data-first
Learns from real experiments, not text
Batch-free
One platform-invariant biological space
Any sample
Place and compare in seconds
Yours to extend
Fine-tune on your private data
What you can ask it — live in Heureka Bench
Stratify any sample
Place an expression profile in biological space and read its likely tissue, disease, and nearest cohort.
Flag mislabels and novel states
Catch when a sample doesn’t match its claimed label — quality control, or the first sign of something new.
Find synthetic-lethal partners
Surface a gene’s most likely synthetic-lethal candidates to guide target and combination strategy.
Link drugs and targets
Find drugs whose response tracks a gene — or candidate targets and response biomarkers for a drug.
Bridge patients to models
Route a tumor or patient sample to its most similar reference cell lines for follow-up experiments.
Context-specific co-expression
A gene’s co-expressed partners within a given tissue or disease — flagged when the link is novel.
Live in Heureka Bench
Ask in plain language inside ARC. Archimedes runs as a set of tools and returns structured, sourced answers — every result tagged with its confidence and caveats.
Train your own
Fine-tune Archimedes on your lab’s data, or build a fully custom model. Your proprietary data stays private while benefiting from our corpus and training infrastructure. Your model, your data, your discoveries.
All your research, one place
- Organize experiments, datasets, and analyses by research question
- Local and cloud storage with workspaces
- Agentic search across all your data and projects
Curate your paper library
- Import by DOI — metadata and abstract auto-extracted
- Read and annotate PDFs inline
- AI summaries, methods extraction, and linking to your projects
See how your ideas connect
- Notes with bidirectional links
- Visual knowledge graph across your research
- AI digests surface patterns and connect ideas you might miss
Track everything in your lab
- Reagents, consumables, and equipment in one view
- Automated low stock alerts before you run out
- Less time on inventory, more time at the bench
Cloud compute, from your desktop
- Run heavy analyses without leaving Bench
- Select data and define the job
- Results sync back to your project
Get your sequencing data, ready to analyze
- Transfer data from FTP/SFTP servers directly to Heureka Cloud
- Auto-detect FASTQ samples — supports 10x Illumina, SRA, and generic naming
- Run scRNA-seq or metagenomics pipelines, results delivered to your project
Scheduled tasks that run themselves
- Define what to analyze — ARC handles the rest
- Literature scans, hypothesis generation, data analysis on schedule
- Results delivered daily, weekly, or custom
From findings to presentation
- Build slide decks from your research
- Slides auto-update when your underlying data changes
- Less time formatting, more time on your research
Published Research
Work Built with Heureka
"Our pathway coessentiality analysis identified specific metabolic pathways linked to complex II, including purine nucleotides. Among ~16,000 genes, loss of nucleotide biosynthesis enzymes strongly sensitized AML cells to complex II inhibition. In a syngeneic AML mouse model, targeting complex II leads to rapid disease regression and extends survival. These findings establish complex II as a central regulator of de novo purine biosynthesis and a promising therapeutic target in AML."
"When we don't use AI, we understand maybe 10% of the message, and 90% of the patterns go unidentified. AI helps us to do that. It helps us identify mechanisms of action... It allows us to fine-tune our approach in terms of interaction with other genes. It also helps us identify the patients most likely to respond... AI is not an advantage for biotech, it's a necessity."
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