Structured observability of institutional communication environments is technically tractable.
Every component — ingestion pipelines, embedding and clustering architecture, rhetorical scoring, policy monitors, and HTML dashboards
Each project below is an end-to-end technical build — from raw ingestion to structured output. They run. They produce real data. I built them to prove I can build yours.
End-to-end pipeline for processing policy documents into a structured Canonical Analysis Object (CAO). Ingests PDFs, chunks and embeds text, runs topic clustering via BERTopic, applies NLI-based rhetorical scoring across six dimensions, and produces three tiers of formatted intelligence reports. Designed for reproducibility — the CAO persists so reports regenerate instantly.
bart-large-mnli, all-MiniLM-L6-v2, distilbert-sst2
Paste any two passages — speeches, policy documents, news coverage — and the analyzer scores each across six rhetorical dimensions: Power, Threat, Moral, Urgency, Us vs. Them, and Legitimacy. Every contributing word is highlighted in context. This is the scoring engine that runs underneath the Narrative Intelligence Pipeline, exposed as an interactive tool.
RSS-based monitor tracking ICE enforcement incidents, court rulings, and executive policy actions. Ingests from curated sources (SCOTUSblog, ACLU, ProPublica, etc.), structures events by overreach category, and produces a formatted weekly brief.
feedparser, pandas, networkx
Longitudinal monitor tracking interstate public health compacts — membership dynamics, narrative volatility, alliance network structure. Produces visualized network graphs and a weekly brief covering policy signal shifts.
OCR-based restoration pipeline for degraded or scanned records. Evolved from Tesseract wrapper to iterative restoration system with image preprocessing and quality assessment.
pytesseract, PIL, pdfplumber
Two working papers documenting the analytical framework and a case study application. Both are archived on Zenodo and carry DOIs. Neither is peer-reviewed.
Introduces Semantic Signal Analysis (SSA), the computational framework underlying NarroVue's pipeline. SSA models discourse as a network of claims rather than operating at the document or sentence level, enabling structural analysis of narratives across heterogeneous corpora.
DOI: 10.5281/zenodo.19470453Case study applying the SSA framework to Project 2025's "Mandate for Leadership" (2023). Draws on 7,397 text segments across 740 sections, annotated with rhetorical framing scores, topic classifications, claim typologies, entity sentiment data, and extracted policy prescriptions.
DOI: 10.5281/zenodo.19470618I can walk into a room with a policy director, understand their workflow, and demonstrate how AI-assisted monitoring would save them hours a week — then go build the first prototype myself.
Everything on this site is verifiable. The DOIs are real. The pipelines run. The methodology is documented. If you want to dig into any of it, I'm happy to walk through it in detail.