Five Systems I Shipped: A Production Case Study Catalog
People ask what I’ve actually shipped. The deep-dive posts cover the systems one at a time; this is the consolidated version. Five systems from the last decade, each one in production, each one moved a number the business cared about. Where there’s a deeper post, I link to it. Where there isn’t, I go a little deeper here.
graph TB
A[Robby<br/>Vertex]
B[Mort AI Nexus<br/>Vertex]
C[Data Cake<br/>Vertex]
D[Voice biometric<br/>JPMC]
E[Strangler-fig<br/>Connect Your Care]
Figure: five systems, five domains.
1. Robby — Multi-Agent SDLC System
Where it ran: Vertex Inc., Data & Insights value stream My role: Inventor, lead engineer Year: 2024–2026
The premise was the super-chicken problem. Teams stacked with high individual performers were producing less, not more. People were spending half their day in process — Jira hygiene, status updates, sprint planning, design reviews — and the half they had left wasn’t enough to do the actual work.
Robby is a patent-pending multi-agent AI system for SDLC orchestration. The bet was that an AI layer could absorb the process work eating my team’s day and give the humans back the hours they came here for. It paid out. The afternoons that used to vanish into Jira hygiene started going back into engineering.
Tech: multi-agent AI tooling, ArgoCD, GitHub Actions, Datadog Outcome: Adopted across multiple teams; the “Raise the Boats” workshop trained ~50 engineers in the live cohort on how to extend the same approach Deeper read: Robby: I Built an AI System to Kill the Super Chicken Problem
2. Mort AI Nexus — Multi-State Economic Nexus Detection
Where it ran: Vertex Inc., Indirect Tax Intelligence My role: Inventor, principal engineer Year: 2024–2026 Status: Patent pending
The Wayfair ruling in 2018 changed sales-tax compliance for every company doing business across state lines. Each state set its own thresholds — typically $100K in revenue or 200 transactions per year — and once you crossed the line you owed sales tax in that state. The catch: most companies didn’t know they’d crossed until a state notice arrived months later, with penalties and interest stacked on top.
The existing approach was reactive. Tax teams reviewed quarterly reports and chased compliance after the fact. Financial exposure for a single missed state could run $100K–$500K.
Mort AI Nexus is a patent-pending AI system that shifts nexus discovery from reactive to predictive — so compliance teams can register and configure tax calculation ahead of crossing a state’s threshold, instead of catching up after a state notice arrives. Customers stopped finding out about exposure from state notices and started finding out from the platform, weeks before the line.
Tech: Java, Spring Boot, AWS Outcome: Customers shifted from reactive quarterly reviews to forward-looking nexus monitoring; preventable penalty exposure reduced
3. Voice Biometric Fingerprinting — JP Morgan Chase
Where it ran: JPMC TARA Fraud Busters, retail banking My role: Lead developer Year: 2018–2019
Knowledge-based authentication was a broken control. Every breached database in the prior decade had handed the answers — mother’s maiden name, last four of the social — to whoever wanted them. A rep could ask all the right questions and authenticate the wrong person.
We built a voice biometric fingerprinting service inside JPMC’s TARA Fraud Busters group. The service captured a passive voice fingerprint during the natural course of a customer call, matched it against the enrolled fingerprint for that customer, and returned a confidence score the rep saw before authenticating. The customer didn’t have to say a passphrase. They just had to talk for a few seconds, which they were going to do anyway.
The deployment numbers told the story. 10 million unique JPMC accounts opted in during the first three months. Only 1,300 customers opted out. That’s a 99.87% adoption rate. The pilot shortened the average authenticated call by 20 seconds because reps weren’t grinding through knowledge-based questions any more — which translated to 25 more customers serviced per rep per day.
The fraud-prevention number is the headline (~$830B in losses prevented across the deployment cohort). The customer-experience side was the quieter win. Security that made the customer’s day shorter instead of longer is the kind of control people don’t opt out of.
Tech: Python, ML/biometrics, REST APIs, Sapiens rules engine, Angular dashboard, JavaScript Outcome: ~10M accounts onboarded in 90 days; 20-second call reduction per authenticated call; ~$830B in fraud losses prevented across the deployment cohort
4. Data Cake — NLP Synthetic Tax Data Generation
Where it ran: Vertex Inc., AI research My role: Inventor, principal engineer Year: 2024–2026 Status: Patent pending
You can’t train tax AI on real customer transactions. The data is PII-laden, regulated, contractually restricted. You also can’t train tax AI without realistic transactional data — synthetic data that doesn’t reflect the statistical properties of real tax events produces models that fall apart in production.
Data Cake is a patent-pending system for generating realistic synthetic tax data that ML projects can train against without putting real customer transactions at risk. Synthetic outputs respect tax-law validity, so downstream models train on something that actually behaves like the production world.
What changed when we deployed it: model training stopped being a regulatory negotiation. New ML projects could spin up training data in days instead of quarters, without ever touching real customer information.
Tech: Python, NLP-driven generation Outcome: Removed the data-access bottleneck for AI model training; enabled tax ML projects that previously couldn’t be greenlit
5. Connect Your Care — Monolith to Microservices
Where it ran: Connect Your Care, HRCommand product My role: Architect & lead engineer Year: 2019–2021
Connect Your Care was a benefits-administration company sitting on a J2EE/EJB monolith built across the better part of a decade. HRCommand was the flagship product. The monolith worked. It served customers. It also blocked every strategic initiative the company wanted to ship — new product surfaces, new payer integrations, modern UX, all of it.
Standard advice for a monolith of that age is rip-and-replace. Standard outcome is a multi-year project that ships nothing while leadership rotates and the rewrite gets killed.
We didn’t do that. We picked the strangler-fig pattern. Identified the bounded contexts inside the EJB monolith that were ready to come out — usually the ones with the most volatility or the clearest data ownership boundary. Built each one as a Spring Boot microservice running alongside the legacy WebLogic/Puppet stack. Routed live traffic to the new service through a thin proxy that fell back to the monolith on failure. Validated for two weeks per service. Then peeled it off the monolith. Repeat.
Five-person team. The React rewrite of the HRCommand UI shipped on Figma-to-code component delivery, so design and engineering were never out of sync on what was being built. We never broke production. We never took the platform down for a release. We did decompose enough of the monolith — and ship enough new product surface on top of the new architecture — that the company became attractive to acquirers in a way it hadn’t been before.
UnitedHealth / Optum Financial acquired Connect Your Care in 2021. The architecture work was a direct contributor to the acquisition thesis. That’s the part of this story I’m proudest of, and the part I’d repeat if I had a stable, profitable platform that needed to be modernized without being broken.
Tech: Spring Boot, J2EE/EJB, WebLogic, Puppet, React, Figma-to-code Outcome: Modernized core product platform without downtime; contributed to UnitedHealth/Optum Financial acquisition
If you’re working on something with the same shape and want to talk it through, I’m at Dominick.do.Campbell@gmail.com, or LinkedIn.
