Foreign-key discovery
Surfaces candidate FK relationships: which column in file A is referenced by which column in file B, with a confidence score per pair.
Discover relationships across datasets — FK candidates, value overlap, semantic links. The ring graph that finds the joins you forgot.
Upload two or more files. Connect runs a deterministic first pass (FK candidates, value-overlap, name Jaccard) and an optional LLM-augmented second pass that proposes semantic relationships across columns the heuristic missed.
Each capability runs in the shared engine — the Norm pipeline, the Trust audit chain, the Decisioning mode toggle. Same substrate as the other four products.
Surfaces candidate FK relationships: which column in file A is referenced by which column in file B, with a confidence score per pair.
Two passes: distinct-value overlap across columns, and name similarity (Jaccard on tokenized column names). Both contribute to the confidence score.
When enabled, Anthropic Sonnet 4.6 proposes additional relationships using column samples + table descriptions. The LLM never sees your raw data — only column profiles and a few sample values per column.
The dashboard renders inferred relationships as chord arcs across a ring of tables. Click any arc to see the underlying evidence: shared values, FK match rate, semantic rationale.
The Python SDK is the most mature. TypeScript follows the same shape. Both ship with strict types and async-first APIs.
Same auth as the rest of DAVA: bearer API key on Authorization: Bearer dava_live_… or session cookie + CSRF for browser flows.
We'll run your hardest dataset through DAVA Connect during a 5-day pilot. You keep the cleaned output and the evidence pack either way.