PRIA

An AI policy-intelligence platform that converts U.S. policy and economic signals into personalized wallet-impact insights.

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PRIA

PRIA (Policy Risk Impact Agent) is an AI policy-intelligence agent that monitors U.S. government and economic signals, then explains how those changes can affect a user's wallet and priorities.

Timeline: 2025-2026.

Project Overview

As Founding Engineer, I built the core product architecture and delivery system that powers PRIA's policy radar, impact analysis, and AI chat workflows. The platform ingests congressional and regulatory activity, macroeconomic indicators, tariff and cost inputs, and user context, then transforms that data into clear and personalized guidance.

Scope and Responsibilities

  • Led engineering for an AI policy-intelligence platform that converts complex legislation and economic signals into user-facing impact insights.
  • Built ingestion pipelines across Congress.gov, Federal Register/OIRA, GovInfo, FRED/FOMC, tariff/HTS feeds, USDA cost data, and user-context inputs.
  • Implemented end-to-end processing workflows: scheduled/background ETL, enrichment, chunking, embeddings/vector retrieval, and personalized scoring.
  • Developed and maintained 70+ production APIs across auth, policy analysis, chat, scoring, search, and financial-integration workflows.
  • Shipped core user experiences in Next.js/React across web, marketing, and extension touchpoints including policy radar, research detail flows, and impact views.
  • Improved auth and lifecycle systems (Clerk flows, middleware routing, transactional/campaign email templates, and delivery infrastructure).
  • Strengthened reliability with CI/CD and infrastructure upgrades across Vercel, Terraform/AWS, Trigger.dev jobs, and backup/disaster-recovery workflows.
  • Drove high execution velocity in a large monorepo: 4,551 commits, 11,071 files touched, and 2.9M+ lines added over ~12 months.

Product Outcome

PRIA made policy complexity actionable by combining high-volume data ingestion with explainable AI output in a user-friendly experience. The result is a system that helps users quickly understand what changed, why it matters, and what to do next.