Wissensgraph (Knowledge Graph)

A knowledge graph shows relationships between customers, orders, parts, and documents that otherwise stay hidden.

About this solution

Dependencies between machine, customer, and part can be traced and queried — not only in one person’s head. For smaller firms often an add-on; for larger data landscapes a dedicated project.

Some questions hang on relationships between data: which customer belongs to which product, which supplier to which order, which asset to which maintenance case?

We build a relationship layer over your data. The technical term is knowledge graph: customers, products, assets, documents, and cases become nodes and links so relationship questions become answerable.

Depending on size, the graph runs as an add-on to RAG or as a larger production project. Hosting is possible with us, on your own on-premises hardware, or in a German cloud.

How it fits together

First we clarify the bottleneck, then we build this solution and connect it to the tools your team already uses.

01
Datenlandschaft
Together we clarify which relationships really matter.
02
Hier
Graph bauen
Data is modeled as traceable relationships.
03
Nutzen
Answers come back via chat, API, CRM, or ERP.

What this can look like

Typical situations from consulting, engineering, and technical teams. Not client case studies.

Beispiel 01

Boutique consulting with fleet customers

Ausgangslage

Projects, contracts, contacts, specifications, and invoices live in separate lists and systems.

Ergebnis

Graph over customers, vehicles, contracts, and cases, queryable for fleet status and next tasks.

Beispiel 02

Project manufacturer with maintenance contracts

Ausgangslage

When a part fails it is unclear which assets, customers, and open orders are affected.

Ergebnis

Knowledge graph over assets, components, maintenance, and customers, with queries for affected cases.

Use cases

Check dependencies between customers, parts, and orders
Extend knowledge search across linked data
Understand impact of changes or outages
Create a foundation for assistants with better context

Integrations

Neo4j and graph databases
CRM and ERP
DMS and data warehouses
REST APIs

Implementation

Week 1–2: You show systems, data fields, and typical relationship questions
Week 3–5: We connect sources and model the graph
Week 6–8: We test queries and check data quality
Week 9–14: Chat or API access, go-live, and handoff

FAQ

Is a knowledge graph too large for small knowledge-intensive companies?

Often yes when you only need a simple search area. It pays off when relationships between data matter more than plain document search.

How is a fixed price possible?

We limit the first graph to a few data sources and concrete questions. A larger production expansion is planned separately.

Where does the graph run?

It can run with us, on your own hardware, or in a German cloud. Data access and permissions are part of the model.

Do we need IT and ongoing maintenance?

To start you need domain knowledge about data and relationships. Maintenance covers new sources, changed fields, and data quality.

Does this replace CRM, ERP, or industry software?

No. The graph connects and explains relationships across existing systems; it does not replace them.

Often pairs with

All services

Book a free intro call

“In 30 minutes we clarify which asset — knowledge, relationships, specifications — justifies a system, and what the smallest useful slice is.”

Julius Haring, founder of aixRun
No obligation and free. One contact from idea to running system.

Free intro call 01516 7732274 [email protected]Contact form

About this solution

Dependencies between machine, customer, and part can be traced and queried — not only in one person’s head. For smaller firms often an add-on; for larger data landscapes a dedicated project.

Some questions hang on relationships between data: which customer belongs to which product, which supplier to which order, which asset to which maintenance case?

Depending on size, the graph runs as an add-on to RAG or as a larger production project. Hosting is possible with us, on your own on-premises hardware, or in a German cloud.

What this can look like

Typical situations from consulting, engineering, and technical teams. Not client case studies.

Beispiel 01

Boutique consulting with fleet customers

Ausgangslage

Projects, contracts, contacts, specifications, and invoices live in separate lists and systems.

Ergebnis

Graph over customers, vehicles, contracts, and cases, queryable for fleet status and next tasks.

Beispiel 02

Project manufacturer with maintenance contracts

Ausgangslage

When a part fails it is unclear which assets, customers, and open orders are affected.

Ergebnis

Knowledge graph over assets, components, maintenance, and customers, with queries for affected cases.

FAQ

Is a knowledge graph too large for small knowledge-intensive companies?

Often yes when you only need a simple search area. It pays off when relationships between data matter more than plain document search.

How is a fixed price possible?

We limit the first graph to a few data sources and concrete questions. A larger production expansion is planned separately.

Where does the graph run?

It can run with us, on your own hardware, or in a German cloud. Data access and permissions are part of the model.

Do we need IT and ongoing maintenance?

To start you need domain knowledge about data and relationships. Maintenance covers new sources, changed fields, and data quality.

Does this replace CRM, ERP, or industry software?

No. The graph connects and explains relationships across existing systems; it does not replace them.

Book a free intro call

“In 30 minutes we clarify which asset — knowledge, relationships, specifications — justifies a system, and what the smallest useful slice is.”

Julius Haring, founder of aixRun
No obligation and free. One contact from idea to running system.

Free intro call 01516 7732274 [email protected]Contact form