Knowledge Graph Compiler

From relational schema to a deployable graph platform.

One compile validates your model, runs 17 generators, and ships a production ZIP — schema, APIs, relational→graph sync, traversals, ontology, and run report.

Start free — 7 days full access Explore live sample

Validated before packaging
Source DB stays read-only
Regenerate on model change

Semantic REPORTS_TO

Verified sample — files

ContosoErp-graphdb.zip Live sandbox

// FK → relationship intelligence
Employee ──REPORTS_TO──▶ Employee
Employee ──HOLDS_ROLE──▶ Designation
Department ──BELONGS_TO──▶ Company

// + constraints, indexes, schema-diff
CREATE CONSTRAINT employee_sqlid …

// Traversal library (generated)

// Impact · shortest path · hierarchy
MATCH p = shortestPath((a)-[*..15]-(b))

// Named intents when semantic layer on
// employee_full_profile · org_chart_full

// Relational → graph mirror
MERGE (n:Employee { SqlId: $id })
SET n.Name = $name …

// Call after ORM SaveChanges
// MERGE pipelines · SqlId sync key

// One ZIP · every layer aligned
Ontology/     semantic taxonomy
GraphDB/      schema · APIs · sync
Queries/      Cypher + repository
Neo4jNative/  Docker import bundle
TypeScript/   typed client
RUN-REPORT    deployment guide

— artifacts 17 generators <3s typical Open sandbox →

01 Model in SQL · Excel · JSON

02 Validate FK graph · diagnostics

03 Compile 17 generators

04 Deploy ZIP · Docker · Neo4j

17 generation passes

— verified artifacts

<3s compile time (typical)

1 deployable package

What you receive

You don't receive templates. You receive a deployable graph application.

Every item below is generated, validated, and packaged from the same domain model — verified on our sample ERP model in the sandbox.

Your SQL schema or Excel model

AutoGraphDB Generation Engine · 17 passes (16 structural + ontology)

Database layer

Knowledge graph model (nodes)
Relationship types
Graph schema (Cypher)
Constraints & indexes

Application layer

Graph repositories
MVC controllers
REST API controllers
Swagger / OpenAPI setup
TypeScript SDK
Postman collection

Operations

Relational → graph sync services
Seeders (FK-ordered)
Docker deployment
Native import bundle
Schema diff & migration

Intelligence & documentation

Traversal query library
Impact · path · hierarchy · orphans
Architecture diagrams
HTML + Markdown run report

Deployable Knowledge Graph Platform

Inspect the sample output Full pipeline walkthrough → How it works

Why AutoGraphDB

Not a converter. A knowledge graph compiler.

Import tools load CSVs. AutoGraphDB compiles your schema into a full developer platform with validated, deterministic output.

Capability	Typical import / ETL tool	AutoGraphDB platform
Load data into graph database	✓	✓ (import bundle included)
Relationship model from FK graph	Manual	✓ Automatic
Production REST APIs + OpenAPI	—	✓ Generated
Relational → graph sync (MERGE pipelines)	—	✓ Generated
Traversal intelligence library	—	✓ Generated
Schema evolution on SQL change	Manual rebuild	✓ Regenerate + diff
Deployment runbook + diagrams	—	✓ Included

Traditional graph project

Week 1–2Graph modelling workshops
Week 3–4Hand-written Cypher + constraints
Week 5–6API + sync development
Week 7–8Testing + deployment
OngoingManual repair when SQL changes

AutoGraphDB platform

<3 secFull knowledge graph platform generated
Same dayDeploy + connect AI workloads
On changeRegenerate — updated sync, queries, APIs
ForeverRepeatable, deterministic, owned by you

Compare output in the sandbox

Traversal library

FK-derived traversals your teams can run today

Generated from your foreign keys and collection relationships — not inferred business semantics. These are the query patterns graph projects spend weeks writing by hand.

Impact analysisConnected entities within N hops

Shortest pathRoute between two nodes

Hierarchy walksSelf-referencing FK chains (e.g. manager → reports)

Per-entity traversalsOutgoing FK + incoming collection queries

Orphan detectionNodes missing expected relationships

Degree & quality checksStructural data-quality queries

Query repositoryTyped async methods per entity

Domain semantic relsREPORTS_TO, HOLDS_ROLE, BELONGS_TO from rule packs

Traversal intentsemployee_full_profile, org_chart_full, and pack-specific queries

ontology.jsonConcept taxonomy + coverage (Pro+)

Semantic layer — rule books, not magic

Starter+ applies domain rule packs (HR, finance, retail, and 7 more) to emit typed relationships like REPORTS_TO and named intents like employee_full_profile. This is curated pattern matching on your schema — deterministic, reviewable, toggleable.

We still do not infer arbitrary business flows from column names alone, and we do not translate natural language to Cypher. Pro adds ontology.json for GraphRAG and governance tooling.

See traversals in the sample output

Schema evolution — your moat

When the database changes, the entire platform regenerates

Every graph consultancy hits this wall. AutoGraphDB is built for the lifecycle — not launch day.

The failure loop (manual graph programs)

SQL schema changes ↓ Graph model drifts ↓ Cypher queries break ↓ Sync services break ↓ APIs out of sync ↓ Developers repair for weeks

The AutoGraphDB regeneration loop

Old schema snapshot + updated model ↓ Regenerate (<3 seconds) ↓ Schema diff + migration Cypher ↓ Updated sync services ↓ Updated REST APIs + SDK + Postman ↓ Updated traversal library ↓ Platform stays aligned — forever

How it works

Three steps to a deployable graph platform

Upload your schema or model

SQL DDL export, Excel worksheet, or JSON — from Postgres, MySQL, SQL Server, Oracle, Supabase, or any validated entity model. Review before you generate.

Generate the full platform

One pass produces ontology, graph schema, sync, APIs, traversal library, import bundle, client bindings, diagrams, and deployment report — under 3 seconds for most models.

Deploy, connect AI, evolve

Run the knowledge graph layer alongside your relational source. Connect GraphRAG, agents, search, and analytics. Regenerate when the domain changes.

Modernize your first schema Format guide →

Enterprise use

Built for architects modernizing at scale

“We didn't need another import tool — we needed the whole relationship layer. AutoGraphDB gave us APIs, sync, and traversals from our ERP schema in one afternoon.”

“Our AI initiative was blocked on graph infrastructure. AutoGraphDB compiled the full platform from our ERP schema — repositories, APIs, SDK, sync, and traversals — in one afternoon.”

“Schema evolution sold it. When product adds modules, we regenerate — we don't rebuild the graph program from scratch.”

Illustrative quotes representing typical buyer outcomes.

FAQ

Questions enterprise buyers ask

Why not use Neo4j Data Importer or a relational→graph ETL tool?

Import tools load data. AutoGraphDB compiles the full platform — sync, APIs, traversals, schema evolution, and deployment artifacts. See the sandbox for verified output.

Does this touch my production database?

No. You upload a schema export or model file. The graph layer deploys separately; sync services are code you control.

What happens when our schema changes?

Update your model and regenerate. Schema diff, sync, APIs, and traversals update together — no manual graph rebuild.

Can we try before we commit?

Yes — sandbox needs no signup. Start a free trial on your own schema when ready.

View all FAQs