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.
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.
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.
Generate the source data
Begin with the structured outputs of the research itself, whatever produced them.
Connect data and models
Move existing research assets into Ounias without rebuilding the upstream process.
Generate complementary analyses
Profile, validate, configure, and explore the dataset across multiple analytical paths.
Enrich, compare, and trace
Connect outputs into a cumulative investigation instead of a collection of isolated results.
Export, integrate, and share
Continue the work in downstream systems or preserve a selected analytical state.
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.