Turn complex datasets into coherent analyses.
Ounias is a no-code environment that generates, enriches, evaluates, and compares analyses derived from structured datasets. Explore complementary methods, preserve every analytical branch, and publish findings with their evidence and context intact.
ProjectionPCA · 2D
Metric pillarsbalanced
Key findings3 signals
Three groups recur across methods.
Feature set A explains the strongest split.
One subgroup remains structurally unstable.
Cross-method finding
Three-group structure persists across five configurations
Bring your fragmented analyses into one central workspace.
Ounias keeps analytical branches, exploration lineage, artifacts, plots, and comparisons together in one easy-to-use environment.
Part of the flow
Ounias uses the data and models you already have and integrates with the tools your team already uses.
01
Generate the source data
Begin with the structured outputs of the research itself, whatever produced them.
Surveys
EHR extracts
Biological assays
Registries
Experiments
02
Connect data and models
Move existing research assets into Ounias without rebuilding the upstream process.
Local files Available
Google Drive Available
Google Sheets Available
MLflow Available
OneDrive Coming soon
Vertex AI Coming soon
03
Generate complementary analyses
Profile, validate, configure, and explore the dataset across multiple analytical paths.
Guided evaluation
Clustering
Supervised analysis
Projection
Manual runs
04
Enrich, compare, and trace
Connect outputs into a cumulative investigation instead of a collection of isolated results.
Cross-method comparison
Label explanations
SHAP
Stability
Longitudinal analysis
Run lineage
05
Export, integrate, and share
Continue the work in downstream systems or preserve a selected analytical state.
Data & artifacts Files
MLflow Available
Snapshots Available
Notion Coming soon
Other platforms Via export
See more in the data and show how you got there.
Ounias combines a glass-box analytical environment, reproducible runs, and direct comparison across methods so findings are easier to understand, challenge, and build on.
Glass-box analysis
Methods, settings, feature roles, artifacts, metrics, and explanations remain visible throughout the investigation.
Reproducible exploration
Dataset identity, seeds, configurations, derived runs, artifacts, and lineage are preserved together.
Comparable evidence
Shared metrics and side-by-side views reveal where methods agree, diverge, or remain sensitive.
Start with the question. Explore what the data supports.
Bring the research question and domain judgment. Ounias handles the analytical workflow, comparison, organization, and provenance—without requiring code or ML engineering.