I build
products,
not just
features.

Bo Clow smiling in front of a wall of green plants open to work ✦ 📍 San Diego, CA

Hi! I'm Bo, a senior full-stack engineer with eight years of experience. Most recently I was the founding engineer at an early-stage real estate fintech startup, where I owned entire product lines from architecture through production. Before that, I spent over six years at GE HealthCare building apps for field teams who couldn't count on a connection. I'm now looking for my next role where I can own a product end to end.

Résumé (PDF) Email me LinkedIn

Stuff I've made.

01 — RetroRate VHS · 2025–26

A Chrome extension that finds hidden 3% mortgages on Zillow.

Assumable loans are invisible on every major listing portal. VHS flags them while you browse Zillow, Redfin, Realtor.com and Compass, and shows the rate and what you'd save, across all 50 states. Owned end to end, live on the Chrome Web Store.

Full story

Assumable mortgages, FHA and VA loans a buyer can take over at the seller's original rate, look identical to everything else on the market. VHS surfaces them in place, with the monthly and lifetime interest savings against today's rates.

As the extension scaled to more traffic and more portals, calling a preview endpoint for every listing became a bottleneck, and fragile, since a portal changing its page could break it outright. I rebuilt it to read each portal's own listing data as it loaded: faster, and resilient to redesigns that used to take it down overnight. From there I took it national, solved the throughput and race-condition problems of checking thousands of listings at once without hammering our backend, and built the internal dashboard we used to see how people actually used it.

Chrome Web StoreProduct page

TypeScript · React · Vite · Chrome Extensions (MV3) · Node

02 — Loan verification · 2026

AI that reads mortgage statements, and a rules engine that keeps it honest.

Agents upload a statement, the terms get extracted and cross-checked, and a public, confidence-scored certificate comes out the other end. It became a paid feature and a new revenue line.

Full story

Verifying that a listing's loan is actually assumable, and at what rate, used to mean trusting whatever the seller's agent claimed. Gemini and Vertex AI extract the terms and reconcile them against a county-recorder cross-check, with anomaly flags for anything that doesn't line up. The certificate is shareable by QR code and expires automatically when a listing sells or goes off-market.

The real challenge was trusting an AI pipeline enough to stand behind its output publicly, which meant building a rules-based confidence engine and a full audit trail instead of taking the model's answer at face value.

TypeScript · Node · Gemini · Vertex AI · GCP · Stripe

03 — MLS partner integrations · 2025–26
50k+

emails a month, from a system that turns each signed MLS partnership into a live product, with its own login, feed and compliance rules.

Full story

Every MLS partnership was a new revenue relationship, and each came with its own login flow, its own listing feed, and its own rules about what we were allowed to say in an email. I built partner-specific OIDC login, a queue that turned raw MLS listings into compliant, on-brand alert emails, and a per-partner rule catalog for thresholds and disclosures. On top of the per-listing alerts I set up an MLS-wide weekly digest campaign.

TypeScript · Node · SendGrid · OIDC · Cron / Queues

04 — Agent dashboard · 2025–26

Turning a listing classifier into a paid agent product.

A model that spots assumable-loan mentions in MLS remarks became the seed of a dashboard agents pay for, and a second revenue line.

Full story

Most MLS listings never mention that their loan is assumable; agents don't know to say it, or don't know it matters. I built a model that reads MLS remarks and flags likely mentions, then used that signal to promote and rank those listings. That grew into a dashboard where agents see how their listings perform, claim and verify them, and see the savings they're marketing to buyers.

Wiring agent identity to MLS IDs so the dashboard showed the right listings, and building the org-level upsell that turned one agent's usage into a brokerage-wide sale, were what turned a detection feature into revenue.

TypeScript · Node · React · Stripe · MongoDB

05 — Search & maps · 2025–26

From a Google Maps prototype to the core product.

The experience most users touch first: split list/map views, nationwide geocoded search, and county- and ZIP-level browsing.

Full story

I took it to production with status-colored markers, geocoded location search backed by a nationwide dataset, and county and ZIP search for buyers who didn't have a single address to start from. The unglamorous part was keeping it working everywhere: a long tail of responsiveness, Safari and mobile fixes, plus the state bugs that only surface once real users stack filters in combinations no spec accounted for.

TypeScript · React · Google Maps · Node

06 — GE HealthCare FX2 · 2021–25 · Tech lead

An app that works in a hospital basement.

10k+

users worldwide on an offline-first field app for iOS, Android and Windows, plus the GraphQL layer I built underneath it.

Full story

FX2 is a business-critical app for field teams who can't count on a connection. I was tech lead from stack exploration and proof of concept through delivery, and built its Apollo GraphQL API from the ground up. It was the company's first GraphQL integration with its major CRM systems, so part of the job was getting teams who had only ever exposed REST to agree on a new contract.

Outside the app I maintained cloud infrastructure behind several critical app suites, migrated an AWS-hosted application to cut its monthly cost by more than 60%, and mentored two engineers.

GraphQL (Apollo) · React Native · React · Java · AWS · Docker

07 — Automation foundry · 2019–21
$1M

saved per year, and 20,000+ hours, from an automation practice I started as a rotational hire.

Full story

GE's Digital Technology Leadership Program rotated me through product management, full-stack engineering and network architecture. In the product rotation I kept hitting the same manual processes duplicated across teams, so I started an automation foundry to attack them and built the internal site that got other teams actually using it. A later rotation had me testing and deploying firmware updates to thousands of live devices in active hospitals.

React · Angular · Java · SQL · Jenkins

08 — Relay (formerly Kompas Care) · 2024–present · Contract

Replacing an SFTP file drop with a real API.

I advised a small business on replacing manual SFTP file ingestion with a REST API, then designed and built it in C#/.NET, migrated existing customers, and wrote onboarding docs for non-technical stakeholders, so onboarding no longer depended on me being in the room.

C# · .NET · REST

09 — Husky Housing Co. · 2026 · Owner & builder

I own a student rental business, and built the site that leases it.

I own and manage Husky Housing Co., off-campus rentals for Michigan Tech students. The site does the leasing legwork: unit listings with availability filters, a map showing the property relative to campus, and an online application, so students can find a place and apply in one visit.

huskyhousing.co

React · Vite · Google Maps · AWS S3

Things I believe.

Start small, learn fast.The first version is how you find out what the second one needs to be.
The details are the product.Architecture earns trust from engineers. Details earn it from users.
Simple scales.The easiest system to grow is the one everyone can understand.

The route so far.

2014–19University of Wisconsin B.S. Computer Engineering & Computer Science
2019–25GE HealthCareLeadership program rotations, then senior engineer and tech lead
2020–23Georgia Tech M.S. Computer Science, Machine Learning
2024–nowRelaySenior engineer, contract
2025–26RetroRateFounding software engineer
NextYour team?Let's talk →

Off the clock.

Spikeball

Technically former professional. The career was brief, the stats are unavailable, and the title is permanent. No further questions.

Snowboarding

Living in San Diego makes this hobby wildly inconvenient.

Latest build 🎯 Raspberry Pi projects

Most recently a DIY Autodarts setup: cameras on the board that detect and score every dart automatically.

Toolbox.

Languages

TypeScript, JavaScript, Python, C#, Java, SQL

Frontend

React, React Native, Vite, Chrome extensions

Backend

Node, Express, GraphQL, REST, background workers

Data

MongoDB, BigQuery, SQL Server, large-scale backfills

Cloud & infra

GCP, AWS, Docker, CI/CD, Terraform, Sentry

AI

Gemini, Vertex AI, LLMs in production paths