Analyze your notes, ideas, PDFs, CSVs, Google search results, surveys, tweets, spreadsheets.
Generate Insights with Knowledge Graphs
Use knowledge graphs to reveal patterns and content gaps. Generate better insights and steer your LLM's reasoning.
Use knowledge graphs to reveal patterns and content gaps.
14-day free trial
Use InfraNodus with: MCP Server · Obsidian Plugin
Step by Step Workflow:
What Are You Working On?
Choose your use case to learn more about how InfraNodus can help you:
Problem
Current AI tools offer generic summaries and shallow insights. They are designed to provide the most likely responses, not to explore new or overlooked perspectives.
Solution
InfraNodus will represent any text as a network and use powerful graph analysis algorithms to identify and highlight the most influential keywords, topics, and their relations. You, the human, can work together with the built-in AI to see patterns, topical clusters, and — more importantly — reveal the blind spots between them.
“I keep wondering about how many weeks or even months I would spend trying to collect all this information and knowledge by myself only.”
Problem
Everyone uses the same tools and competes for the same keywords. What’s often missing is relational insight and a holistic understanding of the context.
Solution
Visualize what people are searching for and compare it to what they find. Find the keyword relations that they are missing. Target those combinations to create high-ranking content. Analyze the content of search results and target the existing gaps with your content. Use the built-in AI to generate content ideas.
“Its utility in SEO and market research, where it identifies gaps between informational demand and supply, is especially notable.”
Problem
Open-ended survey responses are difficult to analyze. Basic positive / negative categorizations fall short, and tag clouds often miss the context.
Solution
InfraNodus visualizes customer feedback as a word graph, where words used within the same response are interconnected. This representation will reveal the patterns and context in your customers' responses and reviews, allowing you to easily identify key topics as well as hidden relationships that other tools might miss.
“We used the Network Structure and interconnectedness features to assist with analysis of a three year university sustainability plan that crossed faculty and staff… Both features were priceless for developing effective cross constituency system design.”
Problem
It’s possible to use an LLM on its own, but it will only give you the most likely response, without a holistic understanding of the context and without a direct connection to your source material.
Solution
You can use InfraNodus in your favorite LLM client like Claude Web or ChatGPT via our MCP server, or connect it to n8n via our official n8n node, and use your knowledge graphs as reasoning experts and as GraphRAG for your LLM. You can use the graph as a steering device to guide the model's attention to the parts of the discourse that you find relevant and to maintain direct connection with the source material to avoid LLM hallucinations.
Built on a peer-reviewed method: Paranyushkin, D. (2019), “InfraNodus: Generating Insight Using Text Network Analysis”, WWW’19 The Web Conference (ACM).
Integrate Knowledge Graphs with Your Favorite LLM or AI Tool
You can integrate InfraNodus in your favorite LLM client, IDE, or automation workflow — and use your knowledge graphs as reasoning experts and as GraphRAG for your LLM.
Connect InfraNodus to ChatGPT + Claude
You can use InfraNodus in your favorite LLM client like Claude Web or ChatGPT via our MCP server.
MCP server: https://mcp.infranodus.com
Set up the MCP connectionConnect InfraNodus to n8n
You can also connect InfraNodus to n8n via our official n8n node and use your knowledge graphs as reasoning experts and as GraphRAG for your LLM.
Knowledge graphs as reasoning experts for n8n
Install the n8n nodeConnect InfraNodus to Cursor
Or connect it to Claude Code, OpenClaw, or your favorite IDE like Cursor or Antigravity by adding the MCP server entry to your configuration file.
VSCode / Cursor IDE Extension
Get the VSCode / Cursor IDE ExtensionConnect InfraNodus to Obsidian
Advanced network science insights for your Obsidian vaults.
Obsidian Graph View Plugin
Get the InfraNodus Obsidian Graph View PluginBrowser extension & API for everything else
How Does InfraNodus Work?
InfraNodus visualizes any text as a knowledge graph. Drawing an analogy from social sciences, words are represented as nodes, while their co-occurrences create the connections between them. This network representation enables the use of advanced graph theory algorithms to identify clusters of related ideas (topic modeling), highlight the most influential concepts and hidden intermediaries, and reveal gaps within the discourse. See how InfraNodus works in detail.
Gregory Bateson coined a beautiful term: “ecology of the mind”. What is a mind that is ecological? It has the ability to have an overview, but it can also zoom in on any idea. It embraces diversity, but it can also obsess over one thing when needed. It can discover the obvious, but it can also reveal the hidden and ponder the gaps that have yet to be bridged. Focused, yet adaptive. Rational, yet poetic. InfraNodus is a tool that is developed to help you think this way. It is made to promote ecological dynamics and diversity on a cognitive level.
To learn more about the algorithms used in InfraNodus and for citations, please, refer to this peer-reviewed paper: Paranyushkin, D (2019). InfraNodus: Generating Insight Using Text Network Analysis, Proceedings of WWW'19 The Web Conference, www.infranodus.com (ACM library, PDF).
Sign Up for a Free 2-Week Trial
Discover the power of ecological thinking and cognitive variability using knowledge graphs and AI-driven insights.
14-day free trial
Pricing Options: Free 14-Day Trial
Your support is what makes it possible for us to develop this
tool. All quotas are per each text. There is no limitation on the
number of texts you can process every month. Prices do not include
VAT for EU private customers.
Lock in current prices for life and save up to 35% (€100-€200) on
our annual plans.
Basic
Best for students and occasional analysis
saving €84 or 35%
- 14-day free trial
- Community support
- Full graph analytics
- MCP server
- Chrome / Firefox extension
- Obsidian graph view plugin
- Import data from web sources
- Max 40 imports per source
- 1 Mb per file upload
- 1Mb per PDF upload
- 40 AI credits / hour
- Personal / Academic use
Advanced
Best for researchers and professionals
saving €204 or 35%
- 14-day free trial
- everything on the Basic +
- API access
- MCP server
- Dedicated support
- 5 Mb per upload
- 5 Mb per PDF upload
- Extended data import quotas
- 80 GPT-5 credits / hour
- Live graph updates (max 5)
- Commercial use
Premium
Best for teams and heavy data work
- 14-day free trial
- everything on Advanced +
- API integration support
- Advanced MCP server
- Training: 1 hour / month
- 10 Mb per upload
- 50 Mb per PDF upload
- Max import quotas
- 200 GPT-5 credits / hour
- Live graph updates (max 20)
- Fast-track required features
Testimonials from Our Users
InfraNodus is used by researchers, consultants, writers, marketing professionals, corporations, and NGOs — Greenpeace, Procter & Gamble, and others. Here's what some of our users have to say:
Become an Expert in Network Thinking
Our
support portal
contains a wealth of information on AI-enhanced text analysis,
network science, data visualization, and their applications. You
can learn how to create new network graphs, interpret them,
discover hidden patterns in your data and share the results of
your research.
You are welcome to contact us at any time via
our Discord community
or by submitting a
support ticket. We are always happy to help you with any questions you might
have and recommend the most effective ways to use InfraNodus for
your research, writing, marketing, or any other purposes.
InfraNodus FAQ
1. What is InfraNodus?
InfraNodus is a visual AI text analysis tool that helps identify patterns and gaps in data. It can be used for brainstorming and ideation, mapping existing knowledge or discourse, studying the market, developing content promotion strategies, and much more.
While it offers standard features such as topic modeling and sentiment analysis, it also provides unique capabilities thanks to its use of a knowledge graph for representation. It can be used both to prime an LLM for better responses and as a heuristic tool for researchers to study content using the graph as a map to navigate the data. You can learn more on the How it Works page.
2. How does InfraNodus compare to other tools?
Most tools are either too technical or too simplistic, and almost none use visualization as a means of exploring data — typically relying on it only to provide summaries and insights. InfraNodus bridges this gap by offering insights usually available only through advanced NLP tools, but within an easy-to-use visual interface.
It also introduces the knowledge graph as an interactive exploratory device that enables both a broad overview and a deep dive into nuanced relationships and content gaps. Its ecological thinking framework fosters cognitive variability, helping users shift attention between scales (zooming in and out) and modes of thinking (focused vs exploratory).
As such, it's not just a research tool, but also a thinking tool that proposes a methodology that is rooted in ecological dynamics. InfraNodus' advanced AI capabilities help generate insights from the knowledge graph and also make them accessible to agentic AI workflows through its API.
3. How to use InfraNodus?
The easiest way to understand how InfraNodus works is to use one of its many content import apps to visualize the existing discourse: for instance, Google search results for a certain topic. The next step is to analyze your own texts or a collection of documents to discover the main topical clusters and content gaps inside.
The basic analysis workflow begins with gaining a general overview of the generated knowledge graph. From there, you explore the main clusters identified through topic modeling and reveal the text statements associated with them, using AI to summarize these statements for a clearer understanding of the core ideas.
Next, the graph can be used to uncover interesting relationships between concepts or highlight content gaps — the blind spots between topical clusters that could be better connected. These areas often hold potential for generating new ideas and insights. The final step involves removing the top layer of already-analyzed concepts to uncover what's hidden beneath, allowing you to reiterate the analysis and discover deeper insights.
4. How to discover the content gaps in your knowledge
You can use the InfraNodus knowledge graph to reveal the main topics in any discourse. Some topics are not very well connected: these are the content gaps that can be used to generate new ideas and insights. More on Discovering Content Gaps
5. How is InfraNodus different from AI chatbots and analysis tools?
AI chatbots return the most likely answer with black-box reasoning. InfraNodus uses AI-powered knowledge graphs instead.
Without InfraNodus:
- Use bulletpoints and long text
- Provide generic responses
- Black-box approach hides reasoning
- No graph, no deeper understanding of context
- Generic LLM responses
With InfraNodus:
- Reveal patterns in your data
- Original insights from the structure
- Interactive graph shows you what's happening
- GraphRAG finds gaps in your knowledge
- InfraNodus MCP augments your LLM
6. How is InfraNodus different from traditional mind mapping?
Manual mind mapping is slow and biased towards what you already know. InfraNodus generates the graph from your text automatically.
Without InfraNodus:
- Limited to hierarchical structures
- Manual creation is slow and prone to bias
- Does not reveal patterns and relations
- Limited import sources
With InfraNodus:
- Systematic approach to text analysis
- Auto-generated knowledge graphs
- Uses network science to reveal unique insights
- Import data from anywhere
7. How is InfraNodus different from standard text analysis tools?
Tag clouds and frequency tables hide the relationships. Text network analysis reveals them.
Without InfraNodus:
- Meaningless tag clouds that lack context
- Only shows positive / negative sentiment
- Results in tables that hide relationships
- Limited interactive exploration
With InfraNodus:
- Reveals main topics, relations, and content gaps
- Sentiment analysis with topic modeling
- Emphasizes relationships through a graph
- Combines metrics with exploration