Scalable knowledge graph development for enterprises
Build a complete knowledge graph – from data aggregation to its visualization on a graph
See our enterprise knowledge graph projects
Look at projects we developed for our clients addressing various businesses goals. Learn the potential of semantic technology , the diversity and wide scope of use cases.
Supporting project managers to build new project teams - Graph Knowledge Base that aggregates information about each worker’s skills and experience
- Input for an application that recommends specialists with the most accurate skills for a new project
- Ability to predict an individual specialist’s development trend based on gathered data
Simplyfing scientific research on medicines - Bioinformatic Protein Knowledge Base powered with machine learning algorithms to process big data
- Effortless data analysis and comparisons, finding correlations and similarities while working on a new cure
- Saving aggregated data in Neo4j and implementation of Apache Beam, Apache Flink and Dask that support big data processing
Tracking learning progress with implemented AI- Front-end for the Educational CMS including handy features such as automatic graph layouts, zoom in/out, and search box
- Enabling rendering of nodes trees consisting of up to 20,000 nodes
- Implementing virtualization that allows you to upload multiple and all of the entities with connectors on a graph at once
Team up with knowledge graph developers
Create an organically expanding knowledge graph architecture by hiring seasoned graph experts – both individuals and full-stack development teams.
10
years on graphmarket
200+
delivered projects40+
graph experts onboard
[…]Their analysts have added value to our project by not only understanding our needs but by proposing better ideas […]. They always try to improve the process of delivery and development, as well as the quality of the code.
Vincent Lapointe Product Manager, OPAL-RT Technologies
“They have great technical knowledge and are very easy to work with.”
Guillaume Bodet CEO, Zeenea
“Synergy Codes is at the top of the charts regarding timeliness, accuracy, completeness, and budget compliance.”
Robert Scott CTO & Co-Founder, EON Collective
These five key steps will help you understand our approach to knowledge graph development process and deliver a complete tool.
WorkshopIf you have only a general goal in mind, during a workshop together we will disover and design what exactly you can achieve. Sometimes it turns out that you don’t need a knowledge graph at all, because other solutions work better for you. And that’s fine! Our developers and product designers will help you discover and define your aim.
Auditing and modifing a data sourceOur data source audit answers the question of whether a data source can power a knowledge graph with updated information. If not, sometimes our experts need slightly adjust data sources. Only then can the database download updated information at specified time periods, connect them, and visualize them on a knowledge graph where they are ready for analysis.
Connecting a knowledge graph with a database Our knowledge graph downloads updated information from multiple connected data sources. All of them are kept in a graph database, which our specialists created using proven and efficient engines like Neo4j or Amazon Neptune.
Defining queries In the next step we explore the aggregated data to further define the queries that the database should answer. Initial queries can be general. The database gives you answers you can explore and, based on them, helps you refine your questions or add new ones. Our graph database extends organically and is scalable, so you can add new information without modifications to its structure.
At this point, the knowledge graph is almost ready. Visualize its data to maximize analysis quality, thereby detecting reapetable patterns and unusual anomalies that become clear. Our product designers’ creative ideas improves usability, lowers the entry threshold for new users, and accelerates the process of getting inisghts to help you take data-driven decisions.
Knowledge graph development with reusable components
Take advantage of premade components that we use to accelerate the creation of a knowledge graph and shorten the time to market.
Automatic layouts
Clean up the layout of content in your graphs. Make them easier to read by changing the composition of objects.
Grouping
Group objects into several clusters to categorize the data displayed on screen, keeping it clean and easy to analyse.
Real-time collaboration
Use one workspace with multiple project members at the same time for quicker delivery and better teamwork.
Zoom in/out
Zoom in and out of your graph to see the details and relationships between different nodes and simplify navigation.
Undo/Redo Manager
Don’t worry about mistakes that you can easily fix with undo and redo functionality. Reverse as many actions as neccessary.
Mini map
Simplify navigation across the graph by using a mini-map that gives you a helicopter view of the whole and easily change location.
Expanding/collapsing nodes
Enhance your graph’s readability. Mask needless data pieces and expose the ones you want to focus on.
Drag&drop
Select objects or sections from the palette, move them to the desired location, and “drop” them there.
3rd-party components
Integrate your existing graph with external sources, whether it’s data from public APIs or specialized tools provided by partners.
Knowledge graph’s potential for data-driven decisions
Check out the capabilities of the knowledge graph built with our development team.
Machine learning algorithms applied to graph data make predicting connections and attributes possible. A graph by itself exposes patterns and visualizes data to simplify analysis. However, applied machine learning algorithms provide better analytical accuracy and faster insights.
Graph data analysis and mining enable the discovery of insights, patterns, and relationships from a network of data points. This process provides organizations with outcomes about, e.g. customer behavior and preferences, allowing them to improve their experience.
Virtualization facilitates uploading a massive number of links and nodes on a graph at once. You can easily navigate through the graph and work on it without waiting for objects to upload. Virtualization helps you work smoothly and quickly on knowledge graphs with massive data volumes.
Visualization simplifies graph-like data analysis. With GoJS visual library, you can transform all aggregated data into a readable knowledge graph. You can enrich it with helpful UI features, such as zoom in/out, mini-map, entity grouping, undo/redo, and others to simplify navigation.
The semantic network supports identifying suspicious activity and fraud detection. Once you define patterns, you can run graph queries using various algorithms to help you identify these behaviors. A knowledge graph can identify fraudulent patterns even when evaluating massive data sets.
To create a scalable knowledge graph, it is necessary to first aggregate data from its database. Data without segmentation, organization, and understanding is useless. Thanks to aggregation, the data becomes more readable and accessible for analysis.
“I appreciate both the code and the product design advisory I received from Synergy Codes”
Daniel Balaceanu Head of Products, DRUID AI
“They’re able to deliver our ideas creatively and efficiently with very few issues and defects”
John Kerry Co-Owner, NextWare GroupGet answers for questions concerning your company
Support your company with organically scalable knowledge graphs that will help understand data gathered in your organization.
- Scalable organic structure that allows you to add new nodes and connectors without impact graph readability
- Efficient performance that lets you render up to 20,000 nodes in milliseconds
- End –to –end service that helps you build a knowledge graph from data aggregation to visualization on a graph