In productionMeter CT09:51:16 Dallas, TX Applied AIForward deployedRevenue systems I build the systems revenue and operations run on, and the AI inside them.
I spent ten years running the CRM, billing, and reporting stack at one company, and I know
exactly where AI projects stall: the systems underneath never quite line up. My work starts
with the operators, inside the stack a business already runs. I add agents where they earn
their place, and I leave behind a system a person can trust: they can see what the AI did,
check it, and stop it. A managed IT provider’s monthly billing has run this way for
about 19 months.
CoHost AI Studio Stricter quality gate on CoHost transcripts
Throughline Retry-queue backoff in Throughline
public 27 extracts on GitHub
based Dallas–Fort Worth · CT · remote/DFW hybridAug 2026
Recommended route for hiring managers One shipped system. One applied-AI system. Then the public code.
Meter shows the billing path in production, about 19 months of it. CoHost shows the quality gates around applied AI. Vouch is the repository you can clone tonight.
Two skill stacks that don’t often overlap.
I keep walking into the same two conversations. The AI side wants someone who has already owned a CRM pipeline and a billing reconciliation. The operations side wants someone who has shipped an evaluated agent into production.
I sit between those lanes. Ten-plus years owning the systems that move work from a new lead to a paid invoice is the base layer: most recently as Head of Technology at Real News Public Relations, sole technical owner across six SaaS platforms and eight-plus production systems since 2018. Internal tools, applied AI, eval harnesses, and human review are the newer layer on top.
It’s my father’s company and I was the only engineer, so my title was whatever we wrote down. I’ve used Head of Technology because it describes the function. Judge the systems, not the org chart.
Where I am not the right fit Pure ML research, ground-up platform infrastructure, Kubernetes platform work, or roles that also require me to own visual design. I work at intermediate level on AWS and Google Cloud and ship with Docker, CI/CD, and managed platforms; I am not the person to build your cluster. I will tell you when a role is a stretch rather than overstate the fit. 10+ yrs
Owning production systems across customer data, billing, reporting, and operations. Full history on
hiring details.
202 accounts
One audit caught a billing bug before the next invoices went out.
self-reported 3 systems
Featured on
work, each written from the actual repository.
27 extracts
Public repositories you can clone tonight. The full list is on the
proof ledger.
Intermedia usage, billed straight into ConnectWise. Moves Intermedia usage into ConnectWise invoices and removes the manual monthly reconciliation. PythonConnectWiseIntermedia ~19 mo in production billing a real MSP
202 accounts in one audit incident (self-reported)
Read the case study
CoHost AI Studio AI automation In progress Nothing publishes until it clears the gate. Checks audio, video, and transcripts before an episode goes live, and holds anything that misses the bar. PythonTypeScripteval harnesshuman-in-the-loop 20+ pipeline steps
11 quality metrics
Read the case study
Throughline Revenue systems Shipped One connector interface, every system in sync. Keeps six vendor tools in sync and puts each client’s information in one reliable place for operations and reporting. PythonPostgreSQLOAuth 2.0retry queues Read the case study The same studio also runs the live TV production automation I built: the operator stays on the live decision.
Most of this work was private until August 2026. I released the repositories one at a time. These three are the place to start.
27 public extracts on the proof ledger
Vouch
Cold-clone reviewed Human review as an API: agents submit work, real reviewers return a consensus verdict.
Holdfast
Cold-clone reviewed An append-only decision ledger with a human publish gate, enforced inside Postgres by triggers, a hash chain, and unique indexes.
Reconciler
Finds billing discrepancies, proposes typed fixes, and requires a person to approve every invoice change.
Continue for more context Architecture writing and how I work
Adapting Cloudflare’s $1 Review Factory for RevOpsAgent systems · RevOps architecture Cloudflare’s code-review factory, adapted to revenue data: coordinator, specialist fusion, risk-scaled compute, and the resilience work needed to make it usable as a demo. Read the article ~7 min read The Four-Method Connector Contract, and Knowing When to StopThroughline · Connector design A four-method connector contract that keeps six vendor integrations behind one engine-owned sync boundary, plus the restraint it took to keep the interface small. Read the article ~10 min read How I work
The layer between AI and day-to-day operations: customer data, billing, reporting, internal tools, quality gates an AI output must clear before it ships, and the workflow logic operators actually depend on. Small surface, strong contracts, practical automation, and a clean handoff to the people who run the process after the engineering work ships. When a process is manual, fragile, or hard to trust; when the data crosses systems that disagree; or when an AI workflow needs a quality bar, a review layer, and an operational owner.