Building the next-gen
neurosymbolic AI platform

We are building deep-tech solutions for neurosymbolic reasoning at lightning-fast speed.

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Our platform

We are building a super-fast platform for data integration and neurosymbolic reasoning.

Icon of Advanced neurosymbolic analytics

02

Tackle demanding real-time use cases

We handle fast-changing data in real time, helping companies react to events instantly – even in the most time-sensitive situations.

01

Advanced neurosymbolic analytics

We combine the strengths of knowledge graphs and flexibility of machine learning to deliver insights you can trust - with answers backed by solid reasoning guarantees.

03

Interoperable by design

Our tech connects easily to other systems and aggregates your data sources while preserving the data’s context and meaning. No ETL needed.

Introducing the first module
of our platform

Jelly – our open-source streaming protocol connecting knowledge graph systems with unprecedented speed.

Try it out

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Blazing-fast by design

Up to 8 times faster than other RDF formats.

Time to transmit 1.4M quads cut from hours to less than 4 seconds in the Nanopublication Network.

Read the full case study

Compatible with your tech stack

Jelly works with Python, Java, Scala, and your favorite RDF libraries. jelly-cli is a fast and convenient CLI tool that lets you start working with Jelly in minutes. You can convert RDF data to Jelly (and back), inspect and validate the resulting Jelly files – all with a single command. It works great in batch jobs and CI pipelines.

See setup instructions on GitHub.

Getting started

Set up with ease — This section guides you through installation, initial configuration, and the essentials you need to begin working smoothly with Jelly.

Quick start - CLI tool

jelly-cli is a simple tool that lets you convert RDF files to and from Jelly, validate Jelly files, and more.

For Linux, macOS, and WSL on Windows, run:

. <(curl -sSfL https://w3id.org/jelly/setup-cli.sh)

jelly-cli

See all available commands and documentation.

Quick start - Java

Jelly-JVM is a super-fast implemention of Jelly, fully integrated with Apache Jena, RDF4J, and Titanium.

To get started, add the following dependency to your pom.xml:

pom.xml

<groupId>eu.neverblink.jelly</groupId>

<artifactId>jelly-jena</artifactId>

</dependency>

See the full Jena user guide.

Quick start - Python

Install the pyjelly package with support for RDFLib:

pip install pyjelly[rdflib]

Write an RDF graph to a Jelly file:

from rdflib import Graph

g = Graph()

g.parse("http://xmlns.com/foaf/spec/index.rdf")

g.serialize(destination="foaf.jelly", format="jelly")

Read a Jelly file:

g = Graph()

g.parse("foaf.jelly", format="jelly")

See the full user guide and API reference.

Other integrations (with or without RDFLib) are available, including Neo4j and NetworkX – see the Python user guide.

Quick start - Apache Jena plugin

Quickly add Jelly support to your Apache Jena Fuseki server (5.2.0+) or other Jena-based applications:

1. Download the Jelly plugin JAR.

2. For Apache Jena Fuseki, place the JAR in the $FUSEKI_BASE/extra/ directory. You may need to create this directory.

3. Start Fuseki and enjoy full support in RDF loading, APIs, SPARQL, and content negotiation.

See the full Jena plugin user guide for more details.

Quick start - Neo4j

You can add Jelly support directly to your Neo4j Desktop or self-hosted server (Community / Enterprise Edition) using our plugin. Alternatively, you can use the rdflib-neo4j integration, which is more limited, but also works with AuraDB.

Installation:

1. Open the Neo4j installation folder.

2. Download the neosemantics plugin .jar file from the neosemantics releases page and place it in the plugins folder. The plugin's version must be the same as Neo4j's.

3. Download the Jelly-Neo4j plugin .jar file and place it in the plugins folder.

4. Restart the Neo4j server.

You may also need to configure your instance for RDF support. See the documentation of neosemantics for details.

Usage:

All features of the neosemantics plugin are supported. For example, to import a Jelly file into your Neo4j instance:

CALL n10s.rdf.import.fetch(

'https://w3id.org/riverbench/dumps-with-results/dev.jelly.gz',

'Jelly'

)

See the full installation and usage guide.

Install the pyjelly package with integration for Neo4j:

pip install pyjelly[rdflib] rdflib-neo4j

You can now import Jelly files into your Neo4j AuraDB or Neo4j server instance:

from rdflib import Graph

from rdflib_neo4j import Neo4jStoreConfig, Neo4jStore

neo4j_store = Graph(store=Neo4jStore(

config=Neo4jStoreConfig(

# add your connection details here

)

))

neo4j_store.parse("foaf.jelly", format="jelly")

See the full installation and user guide.

Use cases

Learn how NeverBlink solves the problems holding your industry back.

Smart city

Energy grid

Smart agriculture

Enterprise AI agents

Smart factory

Autonomous driving

Smart city

Smart cities are held back by poor integration and vendor lock-in. Sensors can’t communicate across platforms, and aggregating their data drives up complexity and cost.

Why our solution?

NeverBlink empowers smart cities with real-time data integration, turning fragmented streams into connected insights that drive faster decisions and better outcomes.

FAQ

NeverBlink is a deep-tech startup specializing in streaming knowledge-graph (KG) and neurosymbolic AI technologies. We combine the structural power of knowledge graphs with the flexibility of machine learning to enable low-latency, real-time data reasoning.
Jelly is an open-source, high-performance binary serialization format and streaming protocol for RDF knowledge graphs. Jelly has implementations for the Java Virtual Machine (works with Apache Jena, RDF4J, and Titanium) and Python (works with rdflib).

You can learn more about what Jelly can do in our documentation. You can also join the Jelly Discord chat to keep up-to-date with the development activities.

The development of the Jelly protocol, its implementations, and supporting tooling was co-funded by the European Union.
NeverBlink products provide advanced neurosymbolic analytics that combine the robustness of knowledge graphs and flexibility of machine learning. NeverBlink lets you derive advanced insights from large amounts of data coming from different sources in domains, in which wrong or baseless predictions are not acceptable.

NeverBlink focuses on real-time use cases, using streaming KG technologies to let companies instantly react to changes.

Finally, NeverBlink products are interoperable by design, built to instantly connect to other systems. They help companies integrate different data sources and avoid risking vendor lock-in. We use W3C standards like RDF and SPARQL as the technical interoperability basis.
We offer tailored consulting and integration services for the first module of our platform, Jelly. We can give you comprehensive support in speeding up your knowledge graph systems, from design, to custom integrations, benchmarking and more.

We can also provide you with early access to our data integration & neurosymbolic reasoning platform, in a co-development model. If you’d like to help us shape this product, please get in touch.

contact@neverblink.eu
jelly-cli is a fast and convenient CLI tool for working with the Jelly knowledge graph streaming protocol. It is compatible with Linux (x86-64, ARM64), macOS (ARM64), or Windows (x86-64). You can use jelly-cli to convert RDF to Jelly, Jelly to RDF, transcode, inspect, or validate Jelly files, all with a single command. See the setup instructions and user guide.

The development of the Jelly protocol, its implementations, and supporting tooling was co-funded by the European Union.

Building the next-gen neurosymbolic AI platform

Our platform

02

Tackle demanding real-time use cases

01

Advanced neurosymbolic analytics

03

Interoperable by design

Introducing the first moduleof our platform

Blazing-fast by design

Compatible with your tech stack

Getting started

Quick start - CLI tool

Use cases

Smart city

Energy grid

Smart agriculture

Enterprise AI agents

Smart factory

Autonomous driving

Smart city

Smart cities are held back by poor integration and vendor lock-in. Sensors can’t communicate across platforms, and aggregating their data drives up complexity and cost.

Why our solution?

FAQ

Building the next-gen
neurosymbolic AI platform

Introducing the first module
of our platform