Turn Unstructured Text into a Graph

80% of your organisation's data is unstructured, and hence unused.

Take unstructured, raw text data and easily build a knowledge graph that with all detected entities and relationships, and leverage more out of your text. In just a few clicks, with no code involved.

Discover how AP-HP turned patient data into a goldmine of information

How AP-HP uses knowledge graphs to structure patient data

Build and enrich your knowledge graph.

With Standupcode, you can find entities and relationships in unstructured data and automatically enrich your knowledge graph with more information.

While most graphs are built using structured, tabular data, with Standupcode you can go one step ahead and start leveraging the rest of the data in your business.

Use Standupcode's highly developed ontology, or use your own.

Use our internal ontology — over 1,000,000 words and concepts about the world — to build your knowledge graph.

Or you can use your own organisation's ontology for more custom use cases. We accept all standard formats.

Use the Graph DB that works for you.

While we use neo4j to visualise the graph on our platform, you can theoretically use any platform you like — TigerGraph, ArangoDB, MongoDB, Amazon Neptune — we can adapt to you.

We are platform-agnostic. The important thing is your access to your knowledge.

Your text made into a graph, step by step:

Here's what we offer in four steps:

1. Raw text import

The first step is to import all the your raw text that you want to use to build your graph.

2. Ontology import

The next step is to import your ontology — including the exact types of relationships and nodes that you want to identify.

3. Ontology alignment

We'll then align your ontology with the one we have internally, to make sure that we use the best of both for maximum accuracy.

4. Text to graph processing

Finally, we'll build the graph database based on all the information in your raw text.

What can you use graph databases for?

Tap into relationships between your data

With graph databases you can gain deeper insight into the relationships between different concepts in your data — see what's connected to what else.

Make higher quality data-driven decisions

Build more accurate predictive models that use the relationships between different aspects of your data to make decisions and predictions.

See our text to graph demo in action

Text to graph

Input any raw text, get a graph out

With Standupcode's text to graph demo, you can input any raw text and automatically generate a knowledge graph out of it.

Business article

Scan a business article, get deeper information

With Standupcode's text to graph demo, you can gather deeper information and insights out of business articles.

Short story

Input a story, understand relationships

With Standupcode's text to graph demo, you can input a story and get easy to understand information on characters, relationships, events, and objects.

Medical record

Input a medical record, get more context

With Standupcode's text to graph demo, you can input a medical record and access deeper and more detailed context about patient's history.

Customer Feedback

The following reviews were collected on our website.

4 stars based on 100 reviews
Excellent Service and Support
The text-to-graph pipeline provided by this company has greatly improved our data visualization process. It's easy to use and very efficient. Our team's productivity has increased by 30% since implementation.
Reviewed by Mr. Ken Hall (Data Scientist)
Highly Recommended Tool
This tool is a game-changer! It simplifies complex data into easy-to-understand graphs, saving us a lot of time. We saw a 25% reduction in the time spent on data reporting.
Reviewed by Mrs. Anne Patterson (Business Analyst)
Great for Data Analysis
The text-to-graph pipeline is a solid tool for anyone needing to streamline data visualization. The setup was straightforward, and our reporting accuracy improved by 20%.
Reviewed by Mr. Jason Fields (Data Engineer)
Good but Room for Improvement
While the tool is effective and reliable, adding more customization options would make it even better. It has improved our data presentation by 18%.
Reviewed by Mr. Jack Connors (Project Manager)
Satisfactory Experience
The text-to-graph pipeline does its job, but we encountered a few minor issues that needed customer support intervention. Despite that, it has sped up our analysis by 15%.
Reviewed by Miss Nicole Choi (Research Analyst)
User-Friendly Interface
The interface is intuitive and easy to navigate. Our team adapted quickly, and our reporting process is now 28% faster.
Reviewed by Mrs. Patricia Hall (Marketing Manager)
Reliable Performance
We rely on this text-to-graph pipeline for all our data visualization needs. It's robust and rarely has any downtime, enhancing our efficiency by 35%.
Reviewed by Mr. Alan Wada (Operations Manager)
A Must-Have for Data Teams
This tool has become essential for our team. It handles large datasets smoothly and presents them in an easily understandable format. Our data processing speed increased by 40%.
Reviewed by Mr. Richard Allen (IT Director)
Effective and Efficient
This tool has streamlined our workflow, making it easier to turn text data into graphical representations. It has improved our analysis speed by 22%.
Reviewed by Mr. John Im (Data Analyst)
Solid Product with Great Support
The customer support team is always ready to help, and the product itself is solid. We've improved our reporting turnaround by 32% since adopting this tool.
Reviewed by Miss Rachel Thomas (Product Manager)

Got Questions? Find Answers Below!

Our Most Frequently Asked Questions

A Text to Graph Pipeline is a process or system that converts unstructured text data into structured graphical representations, such as charts or graphs. This pipeline typically involves natural language processing (NLP) techniques to extract relevant information from the text, followed by data visualization methods to represent that information in a graph or chart format.
The key components of a Text to Graph Pipeline include text preprocessing (cleaning and preparing the text data), information extraction (using NLP techniques to identify key entities and relationships), data transformation (converting extracted information into a structured format), and data visualization (creating graphical representations like charts, graphs, or networks).
Common use cases include business intelligence reporting, sentiment analysis, content summarization, competitive analysis, and market research. By transforming textual data into visual graphs, businesses can quickly identify trends, patterns, and insights.
Technologies commonly used include natural language processing (NLP) frameworks like spaCy or NLTK, data visualization libraries like Matplotlib or D3.js, and machine learning models for entity recognition and relationship extraction. Additionally, data processing tools like Pandas and graph databases like Neo4j may be involved.