Automatic Discovery and Schema Mapping
Miradoris automatically discovers data sources, analyzes their schemas, and maps fields into the unified ontology. Manual configuration is minimized — the platform infers types, relationships, and transformations from the data itself.
Automatic discovery of structure and semantics
When a new data source connects, Miradoris inspects the incoming data stream to infer schema structure, field types, and semantic meaning. Column names, value distributions, and temporal patterns are analyzed to produce a candidate schema without manual intervention.
Context-aware field mapping into the ontology
Detected fields are automatically matched to ontology properties using semantic similarity, naming conventions, and value-range analysis. Ambiguous mappings are surfaced for review with ranked suggestions. Approved mappings are remembered and applied to future sources.
Always current, never stale
Once mapped, data flows continuously from source to ontology. Schema changes in the source are detected and reconciled automatically. New fields are proposed for mapping, removed fields are flagged, and type changes trigger validation checks — all without interrupting the data pipeline.
Continuous monitoring and validation
Miradoris monitors data quality at every stage of the pipeline. Missing values, out-of-range readings, format violations, and duplicate records are detected in real time. Quality metrics are tracked per source and per field, enabling data teams to identify and resolve issues before they propagate.
Be among the first
We are looking for partners willing to test Miradoris in real operational environments. Early adopters get priority access to the platform at significantly reduced rates.
We'll review your request and follow up. No unsolicited contact.