Research & Engineering
Research & engineering notes on how we build Seerly: infrastructure, product engineering, and AI search visibility measurement.
How We Made Sense of Thousands of Keywords
Our keyword clustering experiments were producing 1,189 groups out of 5,874 keywords — fragmented and barely useful. We walked through k-means, HDBSCAN, the Leader algorithm, and finally arrived at Leiden community detection, inspired by the open-source Graphify project.
Beyond Dashboards: A Five-Layer Architecture for Proactive AI Search Visibility
Reactive dashboards log what AI models said. Proactive systems detect why they will change. This paper outlines a five-layer engineering architecture for AI visibility: signal ingestion, influence modeling, narrative drift detection, confidence-weighted recommendations, and governed execution.
Raven: Building a Trustworthy Inference Engine for Production LLM Systems
How we built Raven, an accuracy-first inference engine that uses verification-driven quality loops to produce reliable structured outputs from messy LLM responses.
How LLMs decide who to cite: The mechanics behind AI Search visibility
Citations are now a primary discovery surface, but they remain unreliable enough that visibility without correctness becomes a brand risk. Seerly operationalizes this by tracking citation frequency alongside attribution correctness, so teams can measure where they show up and whether they are represented accurately.