Djinious
One customer view from four systems that disagree
Cluster the records that describe the same organization, and keep the ones you are not sure about out of the merge.
Four systems each hold a version of the same company, with different identifiers, spellings and addresses. Resolution blocks the candidates, scores them pairwise and clusters what clears the line.
What does not clear it stays a candidate for a human, and the analyst’s decision is honoured on every subsequent run rather than re-litigated.
The scores, and the line
- shared identifier
- 1.0shared identifier
- exact normalised name
- 0.85exact normalised nameat or above it, merged automatically
- fuzzy name
- 0.7fuzzy namestays a candidate for an analyst
How it runs
Ingest
Ingest each system through its own connector, keeping its identifiers.
ConnectorsResolve
Run resolution: blocking, pairwise scoring, union-find clustering.
Deterministic matchingDecide
Review the candidates that fell between 0.7 and 0.85 and record a decision.
Analyst decisionsRelate
Materialize edges between the clusters, each carrying the objects it was derived from.
Graph materialization
Keep exploring
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