EnterGenni
Enterprise Knowledge Infrastructure

Make your existing enterprise AI-ready.

Your ERP, CRM, and custom applications already contain years of business knowledge. EnterGenni turns their existing data model into an AI layer you can deploy in a few clicks — without replacing the systems that already run your business.

New: ask your enterprise in plain English, from a ready-made question library, or by drag and drop →

Try your schema free Explore a live example
  • 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
// "Which customers placed the most orders?"
MATCH (o:Order)-[:PLACED_BY]->(c:Customer)
RETURN c.Name AS customer, count(o) AS orders
ORDER BY orders DESC LIMIT 25

// ✓ read-only · checked · export to Excel
// One ZIP · every layer aligned
Ontology/     JSON + OWL/Turtle
GraphDB/      schema · APIs · sync
Queries/      Cypher + repository
Neo4jNative/  Docker import bundle
Ask/          questions · console
Deploy/       Aura · Cloud Run · Azure
RUN-REPORT    deployment guide
— artifacts 17 generators <3s typical Open sandbox →
17 generation passes
— verified artifacts
<3s compile time (typical)
1 deployable package
One upload→ a complete AI layer
Deterministicrepeatable, auditable output
Ask anythingin plain English, read-only
Your cloudwe host nothing of yours
The problem

Your enterprise was built to run the business — not to explain itself to AI.

Every enterprise already has the knowledge: customers, orders, products, employees, suppliers, contracts, transactions. It's just distributed across systems that were never asked to talk to each other.

ERP
CRM
Finance
HR
Supply chain
Documents
Email
Custom apps

AI can read the records in each one. It struggles with how they connect across systems — and that's where most enterprise AI initiatives stall.

The solution

Keep your systems. Connect their knowledge.

EnterGenni gives you an AI layer that sits alongside your existing systems — explicit relationships, shared context, and live sync — so AI can reason across your enterprise instead of guessing between fragments.

Traditional enterprise modernizationEnterGenni
Replace or rebuild systems✓ Keep existing systems
Manually model relationships✓ Derive from foreign keys
Hand-build integrations and AI plumbing✓ Make the enterprise AI-ready in a few clicks
Build APIs separately✓ Generate APIs
Build sync separately✓ Generate sync services
Hand-maintain traversal queries✓ Generate the traversal library
Manually repair after schema changes✓ Regenerate from the updated model
AI becomes another isolated project✓ AI readiness becomes an enterprise capability

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

EnterGenni platform

  • <3 secComplete AI layer generated
  • Same dayDeploy alongside your existing systems
  • On changeRegenerate — updated sync, queries, APIs
  • ForeverRepeatable, deterministic, owned by you

What EnterGenni is not

It's not an ERP or CRM replacement, not a data warehouse, not a generic ETL tool, and not a chatbot or an LLM. It does not infer business logic beyond your schema and curated rule packs, and does not guess at flows a column name doesn't support.

It is the knowledge infrastructure layer that turns your existing relational systems into something AI can reason over.

Compare output in the sandbox
How it works

From your existing systems to an AI-ready enterprise

Understand

EnterGenni reads your existing schema or validated model — SQL DDL (Postgres, MySQL, SQL Server, Oracle, Supabase, Microsoft Fabric), Excel, JSON, CSV, OpenAPI, OData $metadata (Dynamics 365, SAP Gateway), Dataverse entity definitions, SAP dictionary tables and BAPI structures, SAP IDoc definitions or a Salesforce describe export. Several systems at once if you have them.

Map

It identifies entities, relationships, constraints, and structural dependencies from your foreign keys and collections.

Model

It builds a connected knowledge representation of your enterprise — the same domain you already run, made explicit and queryable.

Generate

Graph schema, APIs, repositories, sync services, traversals, ontology, and deployment artifacts — all from that one model. Under the hood: EnterGenni's Knowledge Graph Compiler, 17 coordinated generation passes, under 3 seconds for most models.

Deploy

Run the AI layer alongside your existing systems. Your source database stays read-only throughout.

Connect AI

Give GraphRAG, agents, search, and analytics a connected representation of your enterprise to reason over. Regenerate when the domain changes.

Modernize your first schema Format guide →
Review and connect

Real enterprises run several systems, and some of their names nobody would choose today.

Before anything is generated you can review the names, line up several systems into one picture, and settle types and lengths once. Every change is logged with where it came from and where it went, so you can audit it or take it back.

Review names, with suggestions

Every entity, field and relationship gets suggested names. Accept one with a click or type your own. TBL_CSTMR_MST becomes CustomerMaster, CSTMR_NM becomes CustomerName. AI suggestions (Pro, Enterprise and the trial) see names and types only, never your data. In a hurry? Switch on auto-apply for a project: confident AI names are applied for you, marked in the log, and one click reverts them.

Connect several systems

Upload metadata from Fabric, Dynamics 365 and Dataverse, SAP (dictionary tables, BAPI structures, IDocs), Salesforce, a custom app, or any mix. EnterGenni proposes which entities mean the same thing, shows the evidence, and builds two pictures: AS-IS (every system side by side) and Refined (merged along the links you accept). Nothing merges until a person says so.

Settle types and lengths once

Global rules turn varchar(8000) or money into the type and length your target needs, with a live preview of what would change. Or edit a single field. Generated code carries the lengths and guards values that do not fit.

See four systems become one How review and linking work
Ask

Ask your enterprise questions, without writing queries

Every model comes with a library of ready-made questions, a drag-and-drop query designer, and plain-English drafting. Each question becomes a read-only query you can see before it runs, and the answer exports to Excel, CSV or JSON.

A question library for every model

Hundreds of ready-made questions, grouped by what you want to know: overview, look-ups, connections, hierarchies, data quality, impact, figures and the business intents of your domain. Pick one and run it.

Plain English

Type "Which customers ordered most last quarter?" and get a query drafted from your schema, never your data. You see it, adjust it, then run it. On Pro, and with your own AI key in your generated application.

Drag-and-drop designer

Drop labels, relationships, properties and aggregators such as count, sum and average onto the canvas. The query writes itself as you build, with filters passed safely as parameters.

Export the answer

Download results as CSV, TSV, Excel, JSON or Markdown, or save the query as a Cypher file for your team, your notebook or your BI tool.

Read-only by design

Three barriers stand before your data: a check that allows only reading queries over your own schema, a plan check, and a read-only transaction with a time limit. Credentials and results are never stored.

In your application too

The same question library, designer and exports ship inside your generated code as a console page, behind your own sign-in, so teams can ask questions of production data where it lives.

See the sample's questions How Ask works
What you get

This isn't a template. It's generated infrastructure.

EnterGenni produces the graph model, APIs, sync services, traversal layer, deployment artifacts, and documentation from the same enterprise model — validated and packaged together, verified on our sample ERP model in the sandbox.

Inspect the sample output

Full pipeline walkthrough → How it works

Your schema, model or system metadata
EnterGenni Generation Engine · 17 passes (16 structural + ontology) · plus Ask and Deploy

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
  • Ask endpoint and console

Operations

  • Relational → graph sync services
  • Seeders (FK-ordered)
  • Docker deployment
  • Native import bundle
  • Schema diff & migration
  • Deploy scripts: Aura, Cloud Run, Azure

Intelligence & documentation

  • Traversal query library
  • Question library by intent
  • Ontology: JSON and OWL/Turtle
  • Impact · path · hierarchy · orphans
  • Architecture diagrams
  • HTML + Markdown run report
Deployable AI layer for your enterprise
Deploy

From model to a running database in minutes, in your own cloud

Provision a Neo4j Aura database in your own Aura account straight from EnterGenni, apply your schema, and deploy the generated API with ready-made scripts. You own and pay for everything, and EnterGenni keeps no keys, passwords or data.

Provision on Aura

Paste an Aura API key, choose a size and region, and the instance is created in your account with your model's constraints and indexes applied. The password is shown once, to you. On Pro.

Deploy scripts in every ZIP

A Dockerfile and scripts for Google Cloud Run, Azure Container Apps and Docker Compose, with the database password kept in your cloud's secret store. Provisioning scripts for Bash and PowerShell too.

An ontology your catalogue can read

Pro exports the model as ontology.json and as an OWL 2 ontology in Turtle: labels as classes, properties with their types, relationships with their inverses, ready for Protégé, a triple store or a data catalogue.

How deployment works
The moat

Built for the enterprise lifecycle — not just the first migration.

A graph migration isn't hard on day one. The hard question is what happens six months later, when a table, a field, or a relationship changes. Every graph consultancy hits this wall — EnterGenni is built for what comes after launch day.

The failure loop (manual graph programs)

  1. SQL schema changes
  2. Graph model drifts
  3. Cypher queries break
  4. Sync services break
  5. APIs out of sync
  6. Developers repair for weeks

The EnterGenni regeneration loop

  1. Old schema snapshot + updated model
  2. Regenerate (<3 seconds)
  3. Schema diff + migration Cypher
  4. Updated sync services
  5. Updated REST APIs + SDK + Postman
  6. Updated traversal library
  7. Platform stays aligned — forever
Traversal library

FK-derived traversals your teams can run today

Generated from your foreign keys and collection relationships — not inferred business semantics. This is the connected representation GraphRAG, agents, search, and analytics tools consume — the query patterns graph projects spend weeks writing by hand.

Graph of Employee, Designation, Department, and Company nodes connected by REPORTS_TO, HOLDS_ROLE, WORKS_IN, and BELONGS_TO relationships, with a shortest-path traversal highlighted in cyan

Highlighted in cyan: a shortest-path traversal, Employee → Department → Company (2 hops) — one of the query patterns below, generated automatically.

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 exportontology.json and OWL/Turtle (Pro+)
Question libraryReady-made questions grouped by intent

Semantic layer — rule books, not magic

Starter+ applies domain rule packs (HR, finance, retail, CRM, 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 a question in plain English becomes a read-only query you can see before anything runs. Pro adds ontology.json and an OWL/Turtle export for GraphRAG and governance tooling. EnterGenni gives AI a connected representation of your enterprise to reason over — it doesn't claim to understand your business automatically.

See traversals in the sample output
Enterprise use

Built for architects modernizing at scale

“We didn't need another import tool — we needed the whole relationship layer. EnterGenni gave us APIs, sync, and traversals from our ERP schema in one afternoon.”
Enterprise Architect
Global ERP platform
“Our AI initiative was blocked on graph infrastructure. EnterGenni compiled the full platform from our ERP schema — repositories, APIs, SDK, sync, and traversals — in one afternoon.”
Head of AI Platform
Financial services
“Schema evolution sold it. When product adds modules, we regenerate — we don't rebuild the graph program from scratch.”
VP Engineering
Healthcare data platform

Illustrative quotes representing typical buyer outcomes.

Pricing

Compile once. Scale the program.

Every plan ships a deployable platform ZIP. Upgrade for traversals, semantic layer, seed data, and live deploy.

Free $0 5 runs · 20 entities · prove the compiler Starter $99/mo Traversals, semantic layer, schema evolution, deploy scripts Most popular Pro $499/mo Ask in plain English, Provision on Aura, ontology (JSON + OWL), sync Enterprise Custom Unlimited · on-prem · SSO · SLA
Compare plans & features 7-day full access on trial · No credit card
FAQ

Questions enterprise buyers ask

Does this touch my production database?

No. You upload a schema export or metadata file. The AI layer deploys separately and sync services are code you control. If you choose to run questions on a database from the Ask page, they run read-only and nothing is stored.

Can people ask questions without writing queries?

Yes. Every model comes with a library of ready-made questions grouped by intent, a drag-and-drop query designer, and plain-English drafting on Pro. Each question becomes a read-only query you can see before it runs, and results export to Excel, CSV, JSON, Markdown or Cypher. See the sample questions.

Do you host our data?

No. Provision on Aura creates the database in your own Neo4j Aura account, and the generated deploy scripts run the API in your own cloud. EnterGenni keeps no keys, passwords or data. See how deployment works.

We have several systems with different naming. Can EnterGenni combine them?

Yes. Upload two to eight metadata files, including SQL, OData, Dataverse, SAP dictionary and IDoc definitions, Salesforce, OpenAPI, Excel, JSON or CSV, and review the proposed links between systems. You get an AS-IS picture and a Refined one, with a log of every decision. The enterprise sandbox shows four systems become one, no signup.

What does the AI see?

Only names, types and lengths of entities, fields and relationships, and your question when you ask in plain English. Never row data. AI suggestions are applied only on your click, unless you switch on auto-apply for a project; then each AI-applied name is marked and can be reverted. See the FAQ and privacy policy.

View all FAQs

Your enterprise doesn't need to start over. Start making it AI-ready.

A few clicks turn your existing schema into an AI layer for your enterprise: APIs, connected data, sync, ready-made questions and plain-English answers, verified and ready to deploy in your own cloud alongside the systems you already run.

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