AI-Powered Community Intelligence
An AI-driven platform leveraging IBM Watson and graph theory to let brands identify, analyze, and map niche online communities and real-life influence in real time.
As Founder, I designed and built Audience.ID, an AI-driven community intelligence platform. The goal was to help brands move past basic demographics and map real-life influence and niche communities in real time.
By combining IBM Watson's cognitive capabilities with advanced graph theory, we translated complex social-graph data and unstructured content into actionable, highly legible insights for non-technical marketers.
Role
Founder & Product Designer
Responsibilities
- Product vision & direction
- Community & influencer discovery UX
- Real-time people & posts views
- Social-graph data visualization
Cognitive Analysis
Leveraging IBM Watson for Deep Sentiment
To understand what truly drives niche communities, we integrated IBM Watson to perform multi-modal sentiment and interest analysis. The system analyzed not just text, but also images, content, and user-generated media to extract deep psychological and behavioral segmentation.
This allowed brands to understand the emotional resonance of their content and identify the exact topics, aesthetics, and values that bonded a community together.
Network Architecture
Mapping Influence with Graph Theory
Instead of treating social media users as isolated data points, we used graph theory to map social nodes and calculate real-life influence. By analyzing the connections, density, and flow of information between users, we could identify true community gatekeepers.
This mathematical approach allowed us to distinguish between high-follower "broadcast" accounts and high-centrality "influence" nodes who actually drove conversation and behavior within their niches.
“By mapping social nodes as a dynamic graph, we turned millions of unstructured social posts into a clear map of real-world human connection.”
