InfraNodus
Nodus Labs (operated by InfraNodus SAS) · Leaning · Business
InfraNodus is an AI text analysis tool that turns documents, search results, and notes into knowledge graphs, revealing topical clusters, content gaps, and new angles worth exploring.
About InfraNodus
What Is InfraNodus
InfraNodus is a text network analysis platform from Nodus Labs. You feed it a body of text, and it draws a graph where each important word becomes a node and repeated co-occurrence becomes a connection. Network science then does the sorting: community detection finds groups of related terms, influence ranking finds the concepts holding the graph together, and gap detection finds clusters that should talk to each other but don't.
That last part is the reason people pick it. Most AI writing and research tools summarize what's already on the page. InfraNodus points at the empty space between topics, which is where an unexplored angle, a missing section, or a market opening tends to hide. Researchers use it to map a literature review, SEO teams use it to compare search results against their own draft, and consultants use it to read a competitor's site structure.
The tool gives you a graph and a set of AI prompts built from that graph. It doesn't hand you a finished report. Budget some time to learn what betweenness scores and cluster labels mean, and expect the hourly AI credit ceiling to shape how long a session runs. File size limits on the Basic plan are tight too, so large PDF collections push you toward a higher plan.
Getting Started
- Create an account and start the 14-day trial, then open a new graph from the dashboard.
- Add text: paste it into the editor, upload PDF, TXT, MD, or CSV files, or pull data from Google results, YouTube, a website, an RSS feed, or your Obsidian vault.
- Read the overview first. The graph opens with topical clusters, top keywords, and the structural gaps between clusters.
- Select a gap and ask the built-in AI for research questions, a summary, or ideas that would link the two sides.
- Save the graph, share it with a link, export an image, or connect the MCP server if you want the same analysis inside Claude, ChatGPT, or Cursor.
Product Information
A quick look at InfraNodus's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Academic researchers and graduate students
- SEO and content strategists
- Consultants and market analysts
- Qualitative researchers
Tasks
- Mapping a literature review
- Finding content gaps in search results
- Thematic analysis of survey responses
- Building a knowledge graph for an LLM
Scenarios
- Starting a paper or a long report
- Auditing a client site against competitors
- Planning an article before drafting
Key features
Text Network Visualization
Every text becomes an interactive graph with words as nodes and co-occurrences as edges. Node size follows influence, color marks clusters, and the layout keeps related concepts close together. You can remove hub words to see what sits underneath them, which is how a generic topic turns into a specific argument.
Topical Clusters and Influence Ranking
Community detection groups terms that keep appearing in the same context, so a long document splits into its real themes rather than its chapter headings. That's topic modeling with a map attached instead of a list of words. Betweenness ranking highlights the words that connect otherwise separate clusters. Both views update as you edit, which makes the graph usable as a writing aid and not only as an after-the-fact report.
Content Gap Analysis
InfraNodus measures the structural holes between clusters and ranks them. A wide gap between two well-developed topics means nobody in that corpus is linking them, and that absence is the opening. The built-in AI then writes research questions aimed at the gap, which is more useful than another summary of what's already covered.
AI Insight Generation
The editor carries a chat assistant powered by current frontier models, including GPT-5.4 and Claude Opus according to the company. It reads the graph structure, not just the raw text, so its answers can name clusters, gaps, and influential terms. Credits are metered by the hour: 40 on Basic, 80 on Advanced, and 200 on Premium.
MCP Server and GraphRAG
The hosted MCP server at mcp.infranodus.com plugs the analysis engine into Claude, ChatGPT, Cursor, Codex, and other clients. Your assistant can create a graph, pull the main topics, list gaps, and generate ideas without you writing API calls. For retrieval workflows, the graph acts as structured context, which is the GraphRAG pattern: the model sees relations rather than a pile of chunks.
Imports, API, and Plugins
Data comes in from PDFs, Markdown, TXT, CSV, Google Search and Scholar, YouTube transcripts and comments, Amazon reviews, websites, RSS feeds, and X posts. A REST API exposes graph creation and statistics for custom pipelines, and the official plugins cover Obsidian, VS Code, and Chrome, Firefox, and Safari. Text stays on servers in Ireland under GDPR rules.
Pros and cons
Pros
- Gap detection is the differentiator: the tool shows which topics are missing a connection, not only which topics appear most often.
- The graph doubles as an LLM tool through the MCP server, so the same analysis works inside Claude, ChatGPT, or an IDE.
- Import coverage is broad enough to skip a separate scraper for PDFs, search results, YouTube, Amazon reviews, and RSS.
- Quotas apply per text and texts per month are unlimited, so a large number of small analyses costs nothing extra.
- Data is hosted in the EU and the company publishes its method in a peer-reviewed paper, which matters for academic and client work.
Cons
- There's no free plan, only a 14-day trial, so casual users end up paying €12 per month for occasional checks.
- Basic caps uploads at 1 MB and PDFs at 1 MB with 50 graphs, which is thin for a real literature review.
- AI credits reset hourly, and a long ideation session on Basic will stall at 40 credits.
- Commercial use is blocked on Basic, which catches freelancers who sign up for the cheapest plan.
- Reading the graph takes practice; betweenness scores and cluster labels mean little without the tutorials.
Frequently asked questions
InfraNodus analyzes text as a network so you can see its topic structure and the gaps inside it. Typical uses are literature reviews, SEO content planning, thematic analysis of interviews and surveys, competitor research, and feeding structured context to an LLM.
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