Biological AI training
grounded in proprietary data.

Eigen Bio’s diverse proprietary biological data foundation supports questions rooted in real research. We are expanding coverage across disease contexts, biological modalities, and longitudinal studies as suitable data becomes available.

Available clinical, sample, and study context determines which comparisons a task can support—from differences across cohorts to treatment-related changes and observations over time.

• Proprietary biological dataA growing foundation for original task design, reference analyses, and evaluations informed by Eigen Bio’s research expertise.
• Permissioned partner datasetsComplementary datasets can extend a project where access and the intended use have been agreed with the data owner.
• Public research sourcesSelected public datasets and scientific literature provide additional context and reproducible comparisons where relevant.

Each project specifies its source data and permitted uses, including any training, evaluation, or reuse rights.

Experts turn biological data into problems that require analysis, interpretation, and defensible conclusions.

Each task connects a research question to relevant inputs, a reproducible analysis, and criteria for judging the answer. Subject matter experts develop the reference work, examine the assumptions, and review the complete package.

Our prepared research units pair runnable analyses with executed outputs, decision records, scoring criteria, and documented scientific review and reproduction checks.

• Bioinformatics workflowsAnalysis design, data quality assessment, multi-omics interpretation, and reproducible computational work.
• Biological reasoningEvidence synthesis, mechanistic interpretation, competing hypotheses, and the limits of a conclusion.
• Model evaluationTask-specific assessments, expert scoring, and analysis of the errors that matter for a research workflow.

Begin with a focused task set and a measurable evaluation plan.

We agree on the scientific domain, difficulty, baseline, scoring approach, and acceptance criteria. The pilot gives your team a concrete way to assess task quality, model performance, and delivery fit before expanding the work.

Results guide which tasks to refine, which failure modes to investigate, and whether to expand the engagement.

Work on problems that reward biological understanding and careful analysis.

We welcome expressions of interest from specialists in bioinformatics, computational biology, and related disciplines. Project needs may include task design, reference analyses, rubric development, and independent review.