Building verifiable AI pipelines, browser automation, and data intelligence workflows that replace manual overhead with reviewable execution. Proof surfaces first, promises second.
These are public or sanitized surfaces that show the operating model: collect reliable signals, structure the data, verify the output, and turn it into something usable.
Interactive 3D decision surface over 8,406 publicly available local business records, organized by what businesses do and how they relate.
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A browser-driven audit surface for public web signals like performance, SEO, security headers, and accessibility.
Run the tool
A county-level digital presence snapshot across 1,485 Montgomery County businesses, built to make local digital gaps easier to inspect.
View snapshotAI-assisted lead intelligence workspace: publicly available records, website discovery, audit signals, and prioritized outreach sequencing. 8,406 records organized through repeatable import, cleanup, and QA workflows.
Read the repoThe shape changes by business, but the goal stays practical: less spreadsheet drift, fewer blind spots, and better handoffs between data, decisions, and follow-through.
Public records, website discovery, audit signals, contact paths, and review queues organized into a queryable lead corpus.
Browser-level checks for performance, SEO, accessibility, visible breakage, and security headers across a list or single URL.
Sanitized exports, lead status tracking, sync summaries, and privacy-aware dashboards built from operational data sources.
Scoped model workers for repeatable research, QA, and data processing tasks with taskboard coordination, verification logs, and human-in-the-loop gates.
I prefer narrow, useful builds over vague transformation projects. The first win should expose the next bottleneck instead of locking you into a giant plan.
Map the workflow, data sources, private boundaries, and current failure points.
Pick the smallest system that proves the value and can be verified quickly.
Ship a working tool, dashboard, report, export, or automation lane.
Document how it runs, what it does not do, and where human review stays in the loop.
Useful automation is only impressive when the boundaries are honest. The work is framed as practical systems engineering, not magic.
This page is for the work that sits between web development, automation, data cleanup, and operations. If the pain is messy and recurring, it is probably worth a systems review.
You need a tool built around the real workflow instead of another subscription that almost fits.
You have data but no time to turn it into a useful operating surface.
You need lead intelligence, review queues, or data handoffs without adding a full-time hire.
You want practical AI automation capacity with clear operating boundaries, browser automation, data pipelines, and pragmatic product judgment.
Describe the process that costs time, hides visibility, or keeps falling through the cracks. I will tell you whether it is a good systems build and what the first useful version should look like.
