Connect your data

Turn incoming data into queryable graph context.

RushDB infers property types as data arrives. Nested payloads become linked records immediately. Flat sources can be analyzed for reviewable relationship patterns after import.

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A separate modeling pipeline slows every new data source.

Structured data often arrives as nested API payloads, CSV exports, and flat collections. Rebuilding that context by hand creates scripts that are difficult to keep aligned as the source data changes.

Before

Flatten nested payloads before storage
Maintain schema migration files for each shape
Write one-off join scripts for scattered sources
Repeat the modeling work when fields change

With RushDB

Infer field types as data arrives
Normalize nested objects into linked records
Analyze flat sources for draft relationship patterns
Approve stable patterns before they affect graph meaning

How it works

Start with the smallest useful path.

Import the source shape

Send JSON objects, nested trees, flat arrays, or CSV without designing a separate graph import layer first.

Preserve encoded structure

Nested objects become separate records with default parent-child links. Property types are inferred as values arrive.

Review cross-source patterns

For flat sources, analyze the ontology and approve only the join patterns and semantic relationship names that match your domain.

Implementation sketch

Import nested records in one call.

The nested departments become linked records. Suggested patterns for unrelated flat collections remain drafts until someone approves them.

Know the operational boundary.

Automatic does not mean guessed domain meaning

Nested parent-child structure is available in the payload and can be linked immediately. Cross-source semantics require an approved pattern or an explicit relationship write.

Review suggested relationship patterns