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

What it enables

Ship ingestion before the model is perfect.

RushDB is useful when the first payload is messy and the final data model is still changing. Write the shape you already have, then let the graph become more expressive as relationships are approved.

Accept real payloads

Use nested JSON, arrays of records, or CSV-shaped imports instead of forcing every source through a hand-built staging model.

Keep structure queryable

Nested objects become records connected by default parent-child relationships, so the source structure survives the import.

Evolve flat imports safely

When separate flat sources need joins, suggested patterns stay reviewable before they become durable graph relationships.

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.

Flow

From payload to graph context

The import path follows the docs-backed ingestion pipeline: parse the input, infer field types, assign labels, create records, then wire relationships.

JSON or CSV

Type inference

Records

Relationships

Queryable context

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