John V.
Kenny

Municipal governments sit on decades of as-built drawings, handwritten service records, and construction documents that are trapped in paper or unstructured PDFs — invisible to the GIS systems staff rely on every day.

A suite of computer vision and AI pipelines that extract structured data from these documents at scale — from vision language models that read handwritten water service cards to automated as-built ingestion that pulls metadata, organizes files, and feeds results directly into ArcGIS Pro catalogs, with a human-approval gate before anything enters the catalog of record.

Decades of paper became a living, queryable system — from scanned drawing, to georeferenced GIS feature, to natural-language answer. Higher-quality infrastructure data with traceable lineage back to the source record. Building it stretched me across computer vision, model training, cloud architecture, agentic AI, and RAG.

A lakefront suburb needed residents and visitors to understand downtown parking at a glance — including “how far is this lot from where I'm going?” — and off-the-shelf ArcGIS Experience Builder couldn't deliver that experience.

I designed, built, and deployed a custom React + ArcGIS app: every CBD lot mapped with color-coded restrictions and detailed profiles, on-demand walk-time routing from any origin, and a mobile-first interface — all driven by a single JSON config that makes it portable to other communities.

I owned it end to end — requirements, stakeholder data validation, build, and deployment — with no internal template to work from. Because I built it config-driven, a second municipality was served from the same codebase with no code changes.