What I’ve done

Selected work.

Three live production systems: a real-estate data marketplace, a government procurement-intelligence product, and a small-business operating system. Plus the observability and automation that keep them running. Each comes with a live preview or a walkthrough on request.

Pillar 1 · Live at nidopr.app

Nidopr

The Zillow-style data layer Puerto Rico never had. I took it from raw data to a live product, and it runs itself.

~64,000listings aggregated & served (10+ sources, nightly)
+44% → +5%valuation-model bias after back-testing & trust-gating
~79.6%coverage on calibrated 80% price-prediction intervals
Read the case study →
nidopr.app View live ↗
Pillar 2 · Live at licitapr.com

LicitaPR Inteligencia

Six government systems that don't talk to each other, unified into one procurement-intelligence product that looks ahead instead of back.

~1.24Mgovernment contracts ingested (~90% of universe)
422K → ~345Kvendors resolved to entities (Splink + deterministic)
6procurement categories in the live product
Read the case study →
licitapr.com View live ↗
Pillar 3 · Live on AWS

FT-OS: "La Otra Liga" Business Operating System

A restaurant's website, POS, kitchen screen, inventory, and admin. One system, live on AWS, no per-seat SaaS fees.

4-in-1builder + POS + KDS + inventory, one OS on AWS
148-checkQA battery kept green through a redesign
53-checkoperator-simulation E2E on the visual builder
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La Otra Liga
FT-OS: "La Otra Liga" Business Operating System preview
Private business system. Screenshots; live walkthrough on request.

Curiosity · off the clock

What I build when no one’s paying me.

Same rigor, pointed at a question I just wanted answered. No client, no brief — curiosity run all the way to a live, defensible product.

Live · personal research

databro — reading heartbreak as a season

16 years of US search data, turned into a distress index that knows what it can't prove.

16 yrsof real US Google Trends, Jan 2010 – Jul 2026 (~9,900 monthly records)
4 / 4bucket next-month (t+1) forecasts that beat seasonal-naive AND survive Benjamini-Hochberg FDR (July 2026 refresh)
0.354strongest t+1 out-of-sample skill (reset, Diebold-Mariano p < 0.0001); distress 0.284 at p = 0.0036
Read the case study →
databro.vizlogic.tech View live ↗
Move the month, watch the gauge settle, run the forecast out, then read the caveats sitting right next to the charts.

Have data? Let’s make it think.

Open to Data & AI technical-lead and leadership roles, and to Vizlogic consulting engagements.