Inside GaiaVerse Operations: Our Process

Introduction

Last week, in our first blog post (Welcome to the GaiaVerse Blog), we 1. described our mission and what we do at a high level and 2. introduced this blog. In this blog post, we will dive deeper into what we do, with respect to our business operations, to advance that mission. We will describe the problem we are addressing and our approach to tackling it, including our end-to-end process for projects. We’ll share examples of how our technology is being applied today, as well as where it’s headed next.

The Problem: Fragmented Knowledge in an Interconnected World

Humanity faces global challenges – climate change, resource scarcity, displacement, inequality – that are more interconnected than might meet the eye. Yet information is too siloed to adequately represent interconnected systems, preventing decision-makers from having a systems-level understanding of the world. 

This information fragmentation makes it difficult to see the full picture: to understand how social, environmental, and economic systems interact and evolve together. The result is that even well-intentioned solutions can be ineffective, and further, can create unintended negative consequences.

Our Approach: Modeling the World as an Adaptive Knowledge System

We built GaiaVerse to bridge this gap. As mentioned in our blog post last week, GaiaVerse transforms complex, unstructured information into structured knowledge and reasoning frameworks. In future blog posts, we will explore topics including our technology stack; why we format our information as graphs and store it in a graph database as opposed to tabular data stored in a relational database; the architecture that makes up our reasoning agents and what makes them perform well; and why knowledge graphs and AI agents pair well. For this post, however, we will focus on what we do as a business.

Our Work with Clients

We collaborate with clients across sectors, including research organizations, startups, governments, NGOs, Indigenous groups, and more.

We work with clients to transform their data and other relevant forms of information (e.g., text documents, model outputs, etc.) into a graph schema. Once that schema is defined, we upload the information to our graph database, where it becomes part of the broader GaiaGraph (our system of knowledge graphs) ecosystem – an interconnected network that links client-specific data with external scientific, social, and environmental datasets. Our primary AI reasoning agent, Seeker, traces relationships between local and global systems, identifying dependencies, feedback loops, intervention points, and other patterns. Together with clients, we use these insights to answer targeted questions about their data, diagnose system behavior, predict potential future scenarios, and simulate potential interventions or policy choices.

Our Step-by-Step Process with Clients

1. Understanding: Each engagement starts with a collaborative scoping process.

1. Identifying the Question: We believe the most important step in any inference process is defining the question(s) we seek to answer. We spend as much time as needed with our clients to articulate this clearly, and often help them identify the right question(s) when it isn’t yet clear.

2. Defining the Universe of Discourse: Once the question is defined, we establish the specific scope of information and relationships relevant to answering it. In practice, this means mapping the data landscape, including identifying key entities and relationships. We also work to determine the basic elements of evidence – the data and indicators that will inform the analysis. Along the way, our AI agents can expand this universe of discourse by identifying additional connections or data sources, because a core principle of our approach is recognizing that we cannot know everything in advance.

2. Defining the Graph Schema: Once the system is understood, we define the graph schema for their data and knowledge – the structure that determines how information will be represented as nodes, relationships, and properties. This can be done manually with our team or semi-automatically using our human-in-the-loop Graph Manager agent. Clients can be as hands-on or hands-off as they prefer in this step.

3. Building the Knowledge Graph: After the schema is finalized, we build the knowledge graph in Neo4j, our underlying graph database. The graph can be created through custom Cypher queries or generated through the Graph Manager agent, depending on the project’s complexity and the partner’s level of technical involvement. The graph is connected to the relevant components of the GaiaGraph ecosystem, allowing it to link with external environmental, social, or scientific datasets. This integration gives clients access to a richer context for reasoning and situates their data within a broader systems-level view.

4. Generating Insights with Reasoning Agents: With the graph connected, we deploy AI reasoning agents to analyze its structure and dynamics. These agents trace relationships, detect hidden dependencies, and simulate potential interventions or scenarios.

5. Co-Creating Actionable Decision-Intelligence: Finally, we collaborate with clients to interpret the results and translate them into actionable insights, whether for strategic planning, impact assessment, or system optimization.

Examples of Past and Current Projects

Below are a few examples that illustrate how GaiaVerse’s framework can be applied across different domains. The following examples were chosen to reflect the range of domains and use cases where GaiaVerse’s framework can be applied, from humanitarian work and logistics to personal wellbeing and global environmental leadership.

Global Communities: Improving Water and Sanitation Outcomes in Ghana

As part of an ongoing partnership, we worked with Global Communities to analyze sanitation facilities in Ghana in search of insights into facility malfunctions. Our goal was to determine optimal facility placement and design to avoid such malfunctions considering not only facility longevity but also community impact.

We integrated two years of 1. WASH (Water, Sanitation, and Hygiene) data, including facility locations, maintenance records, and repair histories; 2. MDPI (multidimensional poverty index) data; and 3. qualitative cultural information in the form of text documents. After transforming these datasets into a unified graph schema as part of GaiaGraph, our Seeker agent uncovered spatial and cultural patterns associated with facility malfunctions and identified the most effective intervention points. These included which communities to prioritize, what facility designs would maximize longevity, and where within each community to site new infrastructure for the greatest collective benefit. While we have completed the above, our efforts on this project are ongoing, and we continue to check back in and update our findings as we receive new information.

Kenya Logistics Project: Understanding Anomalies in National Supply Chains

For a government client in Kenya, we worked with siloed logistics data from two years of shipments passing through the Port of Mombasa. The datasets included government records of goods, drivers, routes, and anomaly reports (such as route deviations or tampered seals), which we paired with independent meteorological datasets capturing rainfall, flooding, and seasonal conditions.

Once we modeled these datasets in GaiaGraph and linked them, our Seeker agent was able to reveal causal relationships underlying these anomalies – for example, that route deviations often correlated with flooding events, and that repeated seal breakages traced back to specific handling or training gaps.

This project has since been completed, having provided the client with a foundation for proactive logistics management.

Apple Health Integration: Human Health and Wellbeing Predictive Insights

In an internal pilot project, we linked Apple Health records, Oura Ring data, MacroFactor journal entries, and 23andMe genetic data. By reasoning over this combined graph, the Seeker agent achieved 80% accuracy in predicting nightly sleep quality based on daily behavioral and physiological data patterns. The exercise demonstrated the potential of multi-source personal data graphs for proactive health insights.

Stewards Graph: Mapping Global Environmental Leadership

The Stewards Graph is a large-scale open dataset we developed to connect leaders whose work centers on planetary stewardship. It includes fellows and awardees from organizations such as the MacArthur Foundation, TED, National Geographic, and Echoing Green. Our Co-Founder and President, Moriba Jah – a MacArthur Fellow, TED Fellow, National Geographic Explorer, and professor at UT Austin – is among the leaders represented in the graph.

By linking profiles, disciplines, areas of expertise, projects, and other open-source information, the system can identify individuals working on specific global challenges. For instance, a research consortium focused on coral reef restoration and ocean policy might query the Stewards Graph to find leaders at the intersection of marine science and community engagement.

The same system also helps grantmakers and philanthropists determine where funding can have the greatest impact, pairing committed stewards with potential sponsors.

We’ll close on this example, which captures what drives us at GaiaVerse: uncovering patterns of stewardship and helping people act together, intelligently, across systems.

Ultimately, our goal is to help humanity remember what stewardship means and what it can look like in practice. Because if power is the ability to make choices, then empowering those who choose to steward the planet is the most meaningful form of power we can create.

~The GaiaVerse Team

Up Next

In our next blog post, we’ll dive into our tech stack: the data structures, architectures, and reasoning methods that power our technology. Stay tuned.