Applying structured methodology, source verification, and longitudinal monitoring to how institutions communicate publicly.
I built NarroVue using LLMs as my primary implementation tool. I don't write the code myself. What I do is frame the research question, direct the build, verify the output, and document the method.
The hard part was knowing what to build, breaking a problem down so a model can actually execute it, catching where the output is wrong or just plausible-sounding, and writing the process clearly enough so that I can re-run it. That's the same core discipline behind directing any technical work you don't personally execute by hand: define the problem, manage the build, verify the result.
The skill here is the part people underestimate, framing the problem and sequencing the build. The pipeline ran on a cross-model loop: one LLM wrote each stage, a second reviewed it, and I read the disagreements. I caught the errors by noticing when one model's code failed or didn't match the output I actually wanted. An experienced engineer would catch failure modes I'll miss; that's a real limit and I'm not hiding it. But the boundary runs both ways: the work of deciding what to build doesn't disappear just because someone else writes the code. It's just a different job.
Below is the live analyzer you can test yourself, right now, with no login. The rest of this list is end-to-end builds behind it: real pipelines that ingest, classify, and produce real data, covering source evaluation, structured classification, longitudinal tracking, and document recovery.
Paste any two speeches, policy documents, or news coverage text 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 lexicon scoring engine that runs underneath the Narrative Intelligence Pipeline.
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
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
Similar RSS-based monitor tracking interstate public health compacts including membership dynamics, narrative volatility, and alliance network structure. Includes visualized network graphs and 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 self-archived on Zenodo, which issues a permanent DOI, not a peer-reviewed credential.
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.19470618This project is the clearest evidence I can offer of how I work: define a question, gather and verify sources, structure the evidence, document the method so someone else could reproduce it, and communicate what I found. The habits I bring are close reading of long documents, source collection and organization, checking claims against primary material while I read, and version-tracking my builds so I can show how a project evolved.
Everything on this site is verifiable. The DOIs are real, the pipelines run, and the methodology is documented. I'm glad to walk you through any of it.