The GaiaVerse Technology Stack

Knowledge Graph and Agentic AI for Real-World Decision Intelligence

October 28, 2025

Core Concepts: Knowledge Graphs, Graph Databases, and Agentic AI

Before diving into the layers of our technology, it is essential to define foundational concepts that shape GaiaVerse: knowledge graphs, graph databases, and AI agents. These ideas form the backbone of our approach to representing, reasoning, and acting on interconnected systems. In this section, we define and briefly explain these concepts.

What is a Knowledge Graph? 

A knowledge graph is a graph-structured representation of information in which entities (people, places, things, and ideas) are captured as nodes, while the relationships between these entities are represented as edges. It mirrors the way that real systems operate: as interconnected networks rather than isolated records. 

A knowledge graph:

  1. Represents complex systems intuitively, capturing hierarchies, taxonomies, networks, and dependencies 

  2. Preserves structural and semantic context, enabling deeper reasoning and inference

  3. Supports graph algorithms for network analytics, including centrality, clustering, and pathfinding

  4. Integrates heterogeneous data sources, bringing fragmented or siloed information together into a unified, coherent framework

This approach – representing the information we work with as knowledge graphs rather than as, say, vectors and matrices – allows GaiaVerse to better capture the complexity of real-world systems. This also gives our reasoning agents the structural and semantic context required for meaningful inference (which we will further explore shortly).

What is a Graph Database?

To begin, let's discuss traditional relational databases, which store data in rows and columns, and have long been the default for storing structured data. Relational databases excel in scenarios where data are structured and adhere to a consistent schema, and where relationships in those data are simple and not deeply interconnected. 

But for highly interconnected information, there is often a trade off: 

  1. Computational Efficiency: relationship-spanning queries require multi-table JOIN

  2. Representational Fidelity: relationships live as foreign keys between tables making it hard to store information about the relationships themselves 

  3. Structural Flexibility: real-world systems evolve quickly, whereas rigid schemas require constant refactoring to handle these changes 

Unlike traditional relational databases with tabular data structures, graph databases model reality as an interconnected network of relationships. A graph database provides the infrastructure to store and query graph-structured data – like the graph-based data we maintain. It is built to hold and traverse a network of nodes (entities) and edges (relationships). Graph databases like the one we use, Neo4j, inherently treats relationships between entities as “first-class” data, just as important as the entities themselves making graph databases ideal choices for addressing relationship-centric questions. This matters because graphs: 

  1. Support direct graph traversal (no heavy JOINSs), optimized for exploring multi-hop relationships at scale 

  2. Model properties that describe the nature of relationships, beyond just the relationship’s existence

  3. Adapt structurally as domains evolve and systems change, eliminating the demand for constant schema refactoring 

  4. Lower memory and compute overhead by explicitly storing relationships avoiding runtime JOIN reconstruction and intermediate tables 

  5. Leverage embeddings for nodes and edges that can be vectorized for pattern detection that makes them effective for relational inference 

Graph databases are built for the type of dynamic, relationship-rich data that defines our world. They serve as the foundation that enables GaiaVerse to reason about systems and relationships, transforming connections into valuable insights. 

What is an AI Agent – and Why Now? 

The term “AI agent” has surged in popularity, as more industries explore practical, real-world applications of artificial intelligence. Most people’s experience with artificial intelligence is with Large Language Models, or LLMs, like OpenAI’s GPT, which function by predicting the next most probable word in a sequence using mathematics and statistics. Notably, LLMs alone do not use any reasoning algorithms.

Agents, on the other hand, while built around LLMs, operate with autonomy and are capable of reasoning. An AI agent is an autonomous system that perceives, reasons, and acts within an environment to pursue a goal. More recent versions of ChatGPT, OpenAI’s app that is built on its LLMs, have been agentic. The term “agentic AI” serves as an umbrella term that captures the varying degrees of autonomy among these agents. AI agents fall on a spectrum of autonomy where some agents follow a predefined linear workflow, while more autonomous agents make decisions dynamically within the workflow and even create or select their own tools to accomplish a goal. 

AI agents are designed to have: 

  1. Autonomy as they function without human intervention, and are able to make decisions based on goals, context, and feedback 

  2. Perception to ‘sense’ their environment from different multimodal data inputs (text, images, audio, APIs, etc.) 

  3. Reasoning to analyze information that they gather, draw conclusions, and develop strategies to achieve their goals 

  4. Action to execute tasks based on their reasoning strategies, dynamically selecting the tools or resources needed for each scenario

Think of an AI agent as a highly efficient personal assistant: you set the goal, and the agent gathers what is needed, weighs options, creates plans, takes action, and adapts as conditions change. The value of an AI agent lies in the integration of automation and intelligence, handling the complexity of execution and continuous adjustment so that users can focus on strategic decision making. 

At GaiaVerse, these agents are built on reasoning algorithms that analyze the structure and dynamics of our knowledge graphs to trace relationships, detect dependencies, recognize patterns, and simulate interventions, helping to surface actionable insights and anticipate outcomes so we can improve the world with intention and evidence. In the later sections, we’ll dive deeper into how these reasoning algorithms work, and explore how they understand and act purposefully within complex systems. 

Now that we’ve covered the foundational concepts that shape our technology, let's take these concepts and unpack how they come together within our technology stack, moving step-by-step from raw data to decisions intelligence.

Introduction

GaiaVerse is a decision intelligence platform that uses knowledge graphs, graph databases, and agentic AI to turn fragmented data into data-driven actionable insights. In this post we’ll unpack the key components of our technology - how we ingest, model, and enrich data - to surface quantitative and qualitative knowledge that we can act on.

Data Processing Layer: Preparing Information for Integration

Everything starts with data, often from many different sources, formats, and levels of quality. Before any reasoning can happen, the data must be ingested, cleaned, and normalized so that it can be represented consistently within our system. Our process also incorporates feature engineering, where new context-rich attributes are derived to extend the analytical power of the data. These engineered features help to capture deeper meaning and context, strengthening the performance and robustness of downstream reasoning and modeling. 

We designed our ingestion pipelines to handle structured, semi-structured, and unstructured data alike:

  1. Structured data (e.g., CSVs or relational tables) is parsed, type-checked, and normalized for consistency across entities and unique identifiers. Duplicates and outlines are flagged to ensure accuracy and alignment across all records before integration. 

  2. Semi-structured data (e.g., JSON, XML) is also parsed and standardized to extract entities, attributes, and relationships while preserving its nested logic. Schemas are validated and hierarchical data is flatted to ensure consistency and compatibility when moving the data into a graph schema

  3. Unstructured data (e.g., PDFs, reports, research papers) are extracted for text and segmented (titles, tables, captions), then processed by tagging key entities and their relationships to one another allowing for well-labeled content to be mapped into a knowledge graph. 

This layer transforms messy data into a clean and interoperable foundation. We can move from organizing the information to understanding how the pieces of information connect to one another. We translate this refined data into a knowledge graph where the data begins to function as knowledge.

Technology Stack Overview: From Data to Decision Intelligence

The GaiaVerse stack is designed to move data seamlessly from raw data to structured knowledge, to reasoning, and ultimately decision intelligence. Each layer of this process - data, graph, reasoning, and interface - is modular yet deeply interconnected to enable adaptability across diverse domains and use cases. 

Our stack revolves around four components: 

  1. Graph Manager Agent: our AI agent systems for schema design and data ingestion, enabling efficient organization and retrieval of information

  2. GaiaGraph: an interconnected knowledge graph ecosystem that links open data, private research, and real-time updates to map our world’s complex systems 

  3. The Seeker: our decision-making and reasoning agent that breaks down complex systems to deliver clear, actionable insights 

  4. The Weaver: a data curation agent whose purpose is to provide context to the Seeker’s results; designed to seek out relevant, accurate, and up to date information, ensuring analyses begin with the right data 

We’ll explore how these components fit together, how data are structured by the Graph Manager Agent, flows through GaiaGraph, is reasoned over by Seeker, curated by Weaver, and further evolved by the Graph Manager Agent to create a continuous loop of learning and insight.

Graph Layer: Representing Knowledge as Systems

Once the data have been preprocessed, the next step is to give the data structure and shape it into something that can represent not just the information itself, but an embedded understanding of information: knowledge. This means organizing the data into a knowledge graph to model how entities interact and evolve as part of a larger system. 

All the processed data will eventually flow into GaiaGraph, our internal ecosystem of interconnected knowledge graphs. GaiaGraph links client-specific data with open-source, scientific, environmental, and social datasets to create a broader context that mirrors how our world operates. GaiaGraph is hosted on Neo4j, where we leverage Cypher (Neo4j’s querying language) to analyze the data and explore complex patterns and relationships across the network.  

The process of getting data into GaiaGraph can take two forms: It can be done manually, through collaboration between our data scientists and engineers, who design graph-based schemas and load data into Neo4j using Cypher queries. Or, it can be done semi-automatically through our Graph Manager Agent, that supports human-in-the-loop schema generation and data ingestion. 

The Graph Manager Agent acts as the bridge between human customization and automation. The agents assists in helping to ingest data by interpreting the structure of the data and the defining graph schema, by suggesting entities and relationships based on data patterns. Users can decide their level of involvement, either guiding schema development at each step or allowing the agent to automate ingestion entirely. In all cases, the agent confirms the proposed schema with the user before ingestion to ensure the graph reflects project-specific context. Once approved, the Graph Manager Agent generates and executes its own Cypher queries to load data into GaiaGraph, meaning that users can focus on natural-language queries and insights rather than database operations. This balance helps to ensure flexibility and scalability while preserving precision and alignment with real-world knowledge. 

With the graph established, we move from mapping connections to making sense of them. Leveraging GaiaGraph’s structure and semantics, our agents trace relationships, detect dependencies and surface actionable insights. Next we’ll unpack how reasoning turns connected data into meaningful decision intelligence.

Reasoning Layer: AI Agents and Adaptive Inference

Once our data have been structured into our knowledge graph, the next challenge is to turn those relationships into understanding. This is where our reasoning layer operates, the layer where our agents analyze, infer, and adapt based on the evolving structure of our graph. 

Central to this layer is Seeker, our primary reasoning agent. Seeker operates on top of GaiaGraph, analyzing its structure and dynamics to trace relationships, detect dependencies, and simulate interventions. It explores how and why entities influence one another rather than simply describing what exists. 

Seeker systematically measures the complexity of a query or task and recursively decomposes it into simpler sub-queries until it reaches a baseline level of simplicity. Each sub-query is then assigned a specialized sub-agent that independently investigates the specific piece of the problem. The results are then synthesized into a coherent, evidence-based response to the user’s initial query. This recursive decomposition allows Seeker to scale reasoning across layers of abstraction, maintaining clarity even when navigating highly complex or ambiguous domains. 

Our reasoning framework stands on two philosophical pillars:

  • Popperian Falsifiability keeps every model accountable to reality. Every claim must be testable, disprovable, and open to correction.

  • The Jaynesian Principle of Maximum Entropy ensures that our models never assume more than the evidence allows. When in doubt, we remain maximally open.

Mathematically, Seeker’s reasoning is guided by ARC (Adaptive Recursive Convergence) and CRA (Cascading Re-dimensional Attention), two related frameworks we developed that guide how the agent contracts and expands in its reasoning space. Read our manuscript here.

  1. ARC (Adaptive Recursive Convergence) acts like a semantic microscope that iteratively examines ideas or segments of conversation until it achieves stable understanding or recognizes deeper analysis is necessary. It uses a mathematical process of “contraction” to guarantee that the system reaches a definitive conclusion

  2. CRA (Cascading Re-dimensional Attention) functions as the system’s meta-cognitive awareness. When the ARC determines that the current level of analysis is not sufficient, CRA expands the frame of reference (dimensional escalation) to perceive patterns that are invisible at lower levels. It is like shifting from seeing in only two-dimensions to seeing in three-dimensions, it reveals relationships that had already existed but could not be observed previously 

Together, ARC and CRA allow Seeker to adapt its reasoning to the complexity of the question, zooming in for clarity or stepping back for perspective as needed. This recursive cycle of refinement and expansion allows Seeker to adjust its reasoning by narrowing focus when analysis confidence is high and broadening perspective more context is required. This process leads to convergence, reaching a stable understanding that can be transformed into a clear and reliable output.

Seeker does not treat its answers as absolute. The reasoning layer explicitly models uncertainty, quantifying the confidence of each of its conclusions it reaches. In a world where information is incomplete and contradictory, Seeker accounts for ambiguity by reporting the confidence levels of the evidence behind each of its conclusions, and reconciles its contradictions where it makes epistemic sense to do so. This approach allows Seeker to reason probabilistically and possibilistically, acknowledging that real-world systems are rarely certain, while still providing evidence to support actionable insights even in the presence of uncertainty. In a few weeks, we will publish a blog post that expands on different types of uncertainty and how they can be modeled through probability theory and possibility theory.

Complementing Seeker is Weaver, GaiaVerse’s data curation and contextualization agent. While Seeker focuses on reasoning and inference, Weaver ensures that the information feeding those insights remain accurate, relevant, and up-to-date. It continually searches for new data, cross-validating sources, and identifying inconsistencies. By weaving together diverse datasets, Weaver strengthens the foundation that Seeker reasons upon by providing context to Seeker’s results. 

Together, Seeker and Weaver form the core intelligence of GaiaVerse, one reasoning over relationships, the other ensuring the integrity and context of the data and beneath it. The insights they generate are most powerful when brought to life, enabling people to explore, question, and act on them through our Interface Layer.

Interface Layer: Interaction, Visualization, and Co-Creation

Insights only matter if people can leverage them.

At this layer, users are able to: 

  1. Ask questions in plain language, GaiaVerse supports muti-lingual natural language querying, returning a clear answer grounded in your data 

  2. Explore visually, by working with dashboards and graph views to expand neighbors, trace chains of relationships, and watch patterns emerge from exploration 

  3. Zoom into context. Look into specific entities and relationships to understand how systems in the data connect and evolve over time. 

This layer is designed as a workspace that invites users to question their data and explore, to create understanding with our agents, and leave with decisions that will steward better outcomes for people and our planet.

Bringing It All Together

Each layer of the GaiaVerse stack is powerful on its own, but the real value and impact of it comes from what they build when they work together. We saw how data enters the system and is transformed into a structured knowledge graph within GaiaGraph, then our agents generate insights that users can explore, question, and act on. It is a continuous loop of learning and intelligence. 

Whether we’re supporting humanitarian efforts, environmental stewardship, or health insights, the same system adapts, because this architecture was built to represent any domain as a living network with a shared goal of delivering decision intelligence for our world. 

Our engineers are continually expanding the capabilities of GaiaVerse, improving data ingestion, enhancing reasoning methods, and pushing the stack forward. We designed our technology to learn, adapt, and evolve as new tools and data emerge, and as the world changes. This blog post was meant to give you an overview of our tech stack, and in future posts, we’ll go more in depth into particular components of our technology.

~The GaiaVerse Team

Up Next

In our next blog post, we’ll dive into complex systems theory. We’ll explore how GaiaVerse approaches the study of interdependence, adaptation, emergence, feedback loops, and leverage points across social, environmental, and economic systems.