Research & Engineering
Research & engineering notes on how we build Seerly: infrastructure, product engineering, and AI search visibility measurement.
How We Built Reviewable AI Content Generation with Inline Comments and Brand-Aware Images
Generating a draft is easy. Preserving reviewer intent, brand voice, and visual consistency through every revision is the harder engineering problem.
How We Built a Content Intelligence Loop to Track What Competitors Publish
A useful competitor analysis needs more than a crawler. Here is how we built a content intelligence loop that discovers what competitors publish, separates activity from noise, and turns evidence into a useful weekly read.
From Analysis to Action: Turning AI Visibility Gaps Into a Content Roadmap
A visibility dashboard tells you where your brand is absent from AI answers. It does not tell you what to write next. This is how we built the pipeline that turns prompt-level absence into citation evidence, citation evidence into recurring keyword patterns, and keyword patterns into a deterministic, explainable recommendation, and how narrowing the unit of measurement from a keyword cluster down to a single citation is what made it actionable.
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.