Building Bridges Between Community Data and Actionable Insights: The Role of Knowledge Graphs and LLMs in Missio’s Journey

Hello Indie Hackers,

I’d like to provide a deeper dive into Missio’s (https://www.missio.ai) evolution, our new tool designed to transform unstructured community data into actionable insights. We're addressing a complex issue rooted in our experiences within DAOs and large online communities. I will be going how we use LLMs and knowledge graphs in our product today.

Background

My co-founder and I have a rich history in DAOs (Decentralized Autonomous Organizations). For those unfamiliar, DAOs are organizations represented by rules encoded as a computer program that is transparent, controlled by the organization members, and not influenced by a central government. DAOs are a focal point of innovation in the blockchain space, but they come with a set of challenges. One of the primary issues is the management of vast, dynamic, and often disorganized data generated within the community conversations.

The Aha Moment

Our journey to a solution began in earnest around EthDenver 2023. We realized that Large Language Models (LLMs) and AI could be the antidote to the data chaos. The goal was to transform disorganized data into structured, usable insights, a problem faced by many in the community, not just us.

Initial Product

Our initial attempt to solve this issue took the form of a Discord chatbot for Buildspace S2. Buildspace is a learning platform where people worldwide come together to build real projects in live, online cohorts. The chatbot’s objective was to sift through the extensive communication within the community to identify and categorize ongoing projects. However, the scope of the problem and the potential solution was more extensive than initially anticipated.

The Shift

That realization led us to evolve Missio into a broader tool, one aimed at providing comprehensive analytics for community managers across various platforms, not limited to Discord or Buildspace.

Tech Exploration

Navigating the tech stack was a learning curve. We had a foundational understanding of AI but transitioning that knowledge into a functional, real-world application was a new challenge. We tried various approaches, including using GPT for embeddings and semantic search, but we kept hitting a wall, particularly concerning the user interface and cost structure.

Breakthrough with Knowledge Graphs

Our significant breakthrough came when we delved into the world of knowledge graphs. We used GPT to transform unstructured data into structured information that could be fed into Neo4j, a graph database system. This integration allowed us not only to structure and record responses efficiently but also to execute graph algorithms and queries. This approach offered unique insights into community structures at a base cost, effectively addressing our concerns about the cost being linear to client-side usage.

Where We Stand

We’re now on the cusp of releasing Missio’s Beta. Every feedback loop, every iteration has been a stepping stone to hone a tool that is as practical as it is revolutionary for community managers and members.

For more information and updates on our Beta launch, visit https://www.missio.ai.