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No-code analytical workbench for research data

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.

Analytical workspace · illustrative preview
ProjectionPCA · 2D
Metric pillarsbalanced
Separation
0.82
Explanation
0.78
Stability
0.69
Method fit
0.74
Key findings3 signals
1Three groups recur across methods.
2Feature set A explains the strongest split.
3One subgroup remains structurally unstable.
Cross-method finding
Three-group structure persists across five configurations
Evidence aligned
One connected workspace

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.

01 Projects Keep research contexts and datasets distinct.
02 Datasets Retain identity, versions, roles, and profiles.
03 Runs Organize methods, variations, and artifacts.
04 Comparisons Inspect methods and findings side by side.
05 Evidence Keep plots, metrics, explanations, and lineage connected.
How Ounias fits

A connected flow from research source to reusable insight

Ounias sits between the data and models you already have and the tools your team already uses—adding structure, comparison, and insight without forcing a new research workflow.

01
Research origin

Generate the source data

Begin with the structured outputs of the research itself, whatever produced them.

Surveys EHR extracts Biological assays Registries Experiments
02
Bring work in

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
Investigate

Generate complementary analyses

Profile, validate, configure, and explore the dataset across multiple analytical paths.

Guided evaluation Clustering Supervised analysis Projection Manual runs
04
Build insight

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
Carry work forward

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
Insight & defensibility

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.

Investigate without building the infrastructure

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.