PolicyPath

PolicyPath: Turning Organizational Policies into Connected Intelligence with FalkorDB

Introduction

What if an organization could do more than store its policies? What if it could understand how each policy connects to risks, controls, and responsible people?

That question inspired me to build PolicyPath, a graph-powered policy intelligence platform, for FalkorDB Graph Hacks 2026 under the Company Brain track.

The Problem

Organizations often maintain policies, compliance requirements, risk records, and control documentation separately. Understanding how these pieces relate can take time, especially when someone needs to identify which controls enforce a policy, which risks are associated with it, or who is responsible for approval.

Simply storing documents does not make these relationships easy to explore. I wanted to build a tool that makes organizational knowledge more connected and understandable.

My Solution: PolicyPath

PolicyPath transforms policy information into a connected graph that users can explore through an interactive application.

The project models policies, controls, risks, and owners as graph nodes, with relationships connecting them. This structure helps users investigate policy information and understand how different parts of an organization's knowledge fit together.

The application includes:

• Policy Explorer: Browse and inspect policy information.
• Graph Explorer: Explore connections between policies, controls, risks, and owners.
• Risk Heatmap: Review risk information visually.
• Ask PolicyPath: Ask policy-related questions through a simple assistant.
• Decision Brief: Generate and download structured decision summaries in JSON format.

My goal was to make policy analysis easier to navigate and more useful for decision-making.

Why I Chose FalkorDB

FalkorDB is central to PolicyPath because the project depends on relationships, not just individual records.

I used FalkorDB as the graph database and Cypher queries to retrieve connected information. For example, a policy can be linked to the control that enforces it, a risk it relates to, and the person responsible for approval.

Representing these connections as a graph makes relationship-based exploration a natural part of the application. Instead of treating every policy as an isolated entry, PolicyPath helps users examine the wider context around it.

How I Built It

I developed the application using Python and Streamlit, with FalkorDB handling the graph data. I also used Pandas and Plotly as part of the data and visualization stack.

The process involved designing the graph model, connecting the application to the database, building the exploration interface, and adding risk and decision-support features.

I also deployed the application online and prepared a public GitHub repository and demo video so others could explore the project.

Project links

• GitHub repository: https://github.com/Monika-2108/PolicyPath
• Live application: https://policypath-dnr7owzmvjhxfoib9bcmls.streamlit.app/
• Demo video: https://youtu.be/4WEKiY_XiL8

Challenges and Learnings

One of my biggest learnings was that choosing a graph database is not enough by itself. The graph model and relationships must reflect the actual questions users need to answer.

I learned to think about data as connected entities rather than isolated tables or documents, and to use Cypher queries to retrieve information through those relationships.

I also learned how important it is to connect the backend to a usable interface, deploy an application, document the setup, and communicate a technical idea through a working demonstration.

Building PolicyPath also reminded me that prototypes have limitations. Visualizations, question-answering behavior, and deployment configuration all require testing and refinement. Being aware of those limitations is part of building reliable software.

What's Next?

I would like to extend PolicyPath with richer natural-language querying, more advanced graph analytics, improved visualizations, and more comprehensive policy and risk relationships.

These improvements could help organizations move toward more connected, explainable, and accessible policy intelligence.

Final Thoughts

Building PolicyPath gave me practical experience with graph databases and showed me how connected data can support real organizational workflows.

For me, FalkorDB is more than a place to store records in this project. It provides the relationship layer that makes policy exploration possible.

I hope PolicyPath demonstrates how graph technology can help turn disconnected organizational information into knowledge that people can explore and use to make better-informed decisions.

Built for FalkorDB Graph Hacks 2026 — Company Brain track.