Tell Your LLM How to Think, not What to Think
Create structured cognitive knowledge graphs and reasoning ontologies that will guide your AI's thinking and reasoning.
Watch an Introduction
Watch an Introduction
Why LLMs Struggle with Reasoning
Large language models operate on unstructured text. While they are good at generating fluent responses, they struggle to preserve reasoning structure across steps, apply consistent logic and perspectives, reuse domain knowledge reliably, and explain why a conclusion was reached. Longer prompts and more context help — but only temporarily. To reason well, AI needs explicit structures for concepts, relations, and reasoning rules.
Create a Cognitive Knowledge Graph
From Knowledge Graphs to Reasoning Ontologies
Knowledge Graphs are widely used to represent knowledge and relationships in a structured form that AI systems can reason over. In LLM workflows, knowledge graphs help structure domain knowledge, maintain context across interactions, support GraphRAG and agent pipelines, and improve knowledge representation and reasoning. But knowledge alone is not enough.
Reasoning Ontologies extend knowledge graphs by explicitly modeling types of concepts and roles, relationships and constraints, reasoning paths and perspectives, assumptions, defaults, and alternatives. Instead of only describing what exists, reasoning ontologies describe how thinking should proceed. This makes them especially useful for LLM reasoning control, multi-agent coordination, perspective-based analysis, and transparent, auditable AI outputs.
InfraNodus combines Knowledge Graphs and Reasoning Ontologies into what we call Cognitive Knowledge Graphs — lightweight, ontology-informed concept networks designed to shape reasoning paths in AI systems. They are created in plain text, human-readable and editable, flexible rather than rigid, and designed for reasoning, not data storage. Traditional ontologies aim for formal completeness. Cognitive Knowledge Graphs aim for practical reasoning control.
InfraNodus makes reasoning ontologies visible and explorable. Visual graph analysis helps you see how concepts and reasoning paths connect, detect gaps, blind spots, and missing links, identify dominant assumptions and biases, and compare alternative reasoning structures. This turns InfraNodus into a design and debugging surface for AI reasoning, not just a representation tool. You don't just define an ontology — you test how it thinks.
Use via MCP Server, API, or n8n
Plug-and-play integration with your favorite LLM client, API, or workflow tool. Just use our MCP server:
https://mcp.infranodus.comYou can also connect InfraNodus to n8n via our official n8n node and use our free n8n templates or the simple reasoning n8n template below:
What You Can Build with Reasoning Ontologies
Use InfraNodus to build reasoning ontologies for domain-specific LLMs, multi-agent "panels of experts" with explicit roles, GraphRAG workflows with conceptual grounding, AI systems that explore alternative perspectives, explainable and auditable reasoning pipelines, and tools for reducing hallucinations via constraints.
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Improve RAG Responses
Augment user prompts with contextual data.
Watch the Video -
Plug-and-Play GraphRAG
Improve the quality of retrieval.
Learn More -
Knowledge Optimization
Improve the quality of your LLM context.
Case Study -
Content Gap AI Agent Tools
Detect content gaps and generate questions.
Case Study
Use Case: Building an AI-powered chatbot for a customer support portal
Problem: Your standard AI chatbot won't answer general questions well
Standard AI RAG workflows are not suitable for answering general questions. They will search for the content similar to the user query, but they don't understand the meaning of the questions like "What is it for"?
Solution: Use the InfraNodus GraphRAG API to improve LLM RAG responses
The InfraNodus API will extract the main topics and keywords from your knowledge base (`graphSummary` variable in the `graphAndAdvice` endpoint), which you can then use to augment user prompts and make them more pertinent to the content they query.
Augment User Prompts Case Study Try It Now
Use Case: Teach your LLMs to reason using the GraphRAG / HyperRAG functionality
Problem: Standard LLM responses do not take relations into account
Most LLM worksflows treat text as separate chunks of information and miss out the relations within the text.
Solution: Use InfraNodus GraphRAG to add relational context to your LLM responses
InfraNodus will convert user prompts into graphs and
overlay them on top of the knowledge base graph. It will
then follow the graph's edges to retrieve important
relational context and add it to the LLM context window to
improve the response quality.
GraphRAG Case Study
Try It Now
Use Case: Optimize your knowledge base for LLM context
Problem: Your knowedge base might be incomplete or biased
If the knowledge base that you use to provide the context to your LLM lacks certain topics or is too focused on a specific domain, the quality of responses will suffer.
Solution: Use InfraNodus to optimize your knowledge base with network analysis and visualization
InfraNodus will represent your knowledge base as a knowledge graph and show the main topics, concepts, and relations within. You can use this information to get an overview of the content and identify what's missing. Structural analysis will help identify bias. Content gap detection will reveal the blind spots. Target those to improve the quality of your LLM context.
Knowledge Base Optimization Case Study Try It Now
Use Case: Add creative thinking tools to your AI agents
Problem: Standard LLM agents are not good in detecting content gaps
As a result, they provide generic responses and miss out on important research and marketing opportunities.
Solution: Integrate InfraNodus content gap detection tools into your AI agent workflows
InfraNodus tools can be used by your AI agents to detect content gaps and steer your model towards bridging those gaps to generat new ideas. InfraNodus can also generate research questions that can be used as prompts in your AI workflows.
Crew AI Content Gap Case Study Try It Out
Examples of Cognitive Knowledge Graphs
These are examples of reasoning ontologies and knowledge graphs that can be used to shape how AI thinks and reasons.
Start Designing How Your AI Thinks
Create a Knowledge Graph and Reasoning Ontology you can explore visually and reuse across AI workflows.
Why Use InfraNodus for Reasoning Ontologies?
InfraNodus visualizes any text as a knowledge graph. Concepts are represented as nodes, while their relationships create the connections between them. This network representation enables the use of advanced graph theory algorithms to identify clusters of related ideas, highlight the most influential concepts, and reveal gaps in reasoning structures. This approach transforms how you design and debug AI reasoning systems.
With InfraNodus, you get plain-text ontology and graph creation, visual reasoning analysis and gap detection, bias and perspective awareness, easy integration into LLM workflows, and a system designed for human–AI sensemaking.
InfraNodus helps you design how AI reasons — not just what it
retrieves.
To learn more about the algorithms used, refer to
this peer-reviewed paper:
Paranyushkin, D (2019).
InfraNodus: Generating Insight Using Text Network Analysis,
Proceedings of WWW'19 The Web Conference (ACM library,
PDF).
Ecological Thinking and Cognitive Variability
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 focus 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 creative.
InfraNodus applies these principles to AI reasoning. It helps you create ontologies that promote ecological dynamics and cognitive variability — preventing AI systems from getting stuck in narrow reasoning patterns while maintaining coherent structure across complex workflows.
Start Designing How Your AI Thinks
Create a Knowledge Graph and Reasoning Ontology you can explore visually and reuse across AI workflows.
Pricing Options
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.
Advanced Account
saving €204 or 35%
- 14-day free trial
- everything on the Basic +
- API access
- Dedicated support
- 2 Mb per upload
- 5 Mb per PDF upload
- Extended data import quotas
- 100 GPT-4 credits / hour
- Live graph updates (max 5)
- Commercial use
Basic Account
saving €84 or 35%
- 14-day free trial
- Community support
- Full graph analytics
- Chrome / Firefox extension
- Obsidian graph view plugin
- Import data from web sources
- Max 40 imports per source
- 300 Kb per file upload
- 1Mb per PDF upload
- 40 GPT-4 AI credits / hour
- Personal / Academic use
Premium Account
- 14-day free trial
- everything on Advanced +
- API integration support
- Training: 1 hour / month
- 10 Mb per upload
- 50 Mb per PDF upload
- Max import quotas
- 500 GPT-4 credits / hour
- Live graph updates (max 20)
- Fast-track required features
InfraNodus vs. Traditional Ontology Tools
Traditional ontology tools are designed for formal knowledge representation, not for shaping AI reasoning. InfraNodus provides a unique approach that combines the structure of ontologies with the flexibility needed for practical AI workflows.
traditional tools
- Formal ontologies require specialized expertise to create and maintain
- Focus on data storage, not reasoning control
- Rigid structures that don't adapt to context
- No visual debugging or reasoning analysis
- Difficult to integrate with LLM workflows
- Cannot detect gaps or biases in reasoning
InfraNodus
- Plain-text ontologies anyone can create and edit
- Designed for reasoning control, not just data storage
- Flexible structures that adapt to different contexts
- Visual graph analysis to debug and refine reasoning
- Easy export to prompts, agents, and LLM workflows
- Detects gaps, blind spots, and biases automatically
Testimonials from Our Users
Researchers and AI practitioners use InfraNodus to design reasoning ontologies and cognitive knowledge graphs for their AI workflows. Here are some of their testimonials:
Learn How to Build Reasoning Ontologies for AI
Our
support portal
contains tutorials on creating cognitive knowledge graphs,
reasoning ontologies, and integrating them with LLM workflows
using tools like Dify.AI, Crew AI, and more.
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 design reasoning structures
that shape how your AI thinks.
1. How to Create a Reasoning Ontology
Use AI to generate a cognitive knowledge graph for any domain, or import existing text, documents, or web content. InfraNodus converts your content into an ontology-informed structure designed to shape AI reasoning. More on Creating Reasoning Ontologies
2. How to Analyze and Debug Reasoning Structures
Visual graph analysis helps you see how concepts connect, identify dominant assumptions, and detect biases in your reasoning structure. You don't just define an ontology — you test how it thinks. More on Reasoning Analysis
3. How to Integrate with LLM Workflows
Export your reasoning ontology as plain text for use in prompts, agents, and automation workflows. The same structure serves both human sensemaking and AI execution. More on LLM Integration
4. How to Detect Gaps and Blind Spots
InfraNodus automatically identifies gaps, blind spots, and missing links in your reasoning structure. Use these insights to refine your ontology and ensure your AI considers alternative perspectives. More on Gap Detection