I am trying to understand what changes when AI stops being a chat box and starts becoming part of how work actually gets done.
That question shows up everywhere for me: in customer workflows, partner integrations, field teams, research loops, personal systems, small-business operations, and the tools I build for myself. I am interested in AI as an operating layer, not a novelty. The useful work is figuring out where it belongs, where it does not, how to measure it, and how to keep it from creating new messes faster than it solves old ones.
My background is enterprise solution engineering and partner engineering. I have worked with regulated enterprise customers, technical partners, SI/GSI teams, sales teams, product teams, and operators who need software to solve a real business problem. That background shaped how I think about AI: the model is rarely the whole system. The workflow, data, incentives, review points, integration path, and proof of value matter just as much.
The work I am most drawn to now looks a lot like forward-deployed AI:
- sit close to the people doing the work
- map the workflow until the real constraint is visible
- decide what should be automated and what should stay human
- build the smallest useful system over the tools already in place
- add evidence, evals, review, and rollback before trusting autonomy
- explain the technical path in business language without hiding the failure modes
I want to build AI systems that make operators meaningfully faster, not dashboards that make teams feel modern for a quarter.
That means I care about:
- agents that can use tools without being given unlimited trust
- evals that test the actual job, not just the final answer
- acceptance layers that tell an operator whether the work is done
- workflow audits that separate real leverage from automation theater
- integrations that respect the systems a company already depends on
- AI products that are boring enough to run every day
An acceptance layer for validating agentic completion against user intent and evidence.
Proofline exists because agent summaries are not enough. If an agent claims a task is complete, there should be a way to inspect the evidence, compare the result to the original intent, and decide whether the work actually holds up.
The project is a bet that the next useful layer in agentic work is not another prettier chat UI. It is the control plane around the work: evidence, completion checks, escalation paths, and a way for humans to trust outputs without pretending uncertainty disappeared.
Writing on applied AI, agent operations, workflow automation, and production failure modes.
The Lab is where I pressure-test the ideas behind the systems I am building: where human review belongs, why queues matter, what makes agent work auditable, when AI is the wrong answer, and how to tell the difference between a demo and something an operator can depend on.
An applied AI consulting practice focused on practical automation for small businesses and teams without in-house AI engineering capacity.
The motion is intentionally direct: audit the workflow, identify the repetitive work that actually matters, build a useful system, deploy it into the existing operating environment, and support it after launch.
Some of the most relevant work is private, unfinished, or tied to operating context that should not be public:
- AI workflow audit and research systems
- local-first SaaS research and role intelligence tools
- agent dashboards and operating-control prototypes
- merchant operations rescue queues for inventory and feed issues
- internal agent memory, review, and execution systems
- collaborative planning and automation products
I keep this work private when it contains client context, product direction, or operational details. The public projects and writing show the same bias: practical systems, visible evidence, and less tolerance for AI theater.
Most failed AI projects do not fail because the model is weak. They fail because the workflow was misunderstood, the integration path was brittle, the success criteria were vague, or the system had no answer for what happens when the agent is wrong.
My default approach:
- start with the workflow, not the model
- use AI only where the ambiguity justifies it
- prefer structured outputs and explicit tool boundaries
- log prompts, tool calls, decisions, and evidence
- start with the smallest unit of autonomy
- evaluate against real examples, not synthetic confidence
- put human review exactly where risk enters the system
- Senior Solutions Engineer and Partner Engineering at Quantum Metric
- Enterprise financial services and insurance solution engineering
- Technical partnerships, integrations, SI/GSI enablement, and co-sell support
- Prior roles across solution engineering, education, services, integration engineering, and systems administration
- MBA, University of Tennessee, Knoxville