Your operation outgrew the spreadsheets, the inboxes, and the one person who knows how it all works. I figure out the root of the problem, we align on what the right set of deliverables should look like — software, a dashboard, an analysis, a strategy, some mix — and hand back something your team runs without me.
A handful of consulting engagements. Descriptions stay pattern-level — sensitive specifics, regulated data, and contractual details live in the private source material.
How to read these — deterministic where the facts matter, AI-assisted where the language matters. The durable part is rarely the model. It's the framing of the workflow, the review loop, the trust of the people who'll run it, and turning ambiguity into something somebody can actually operate on a Monday morning.
Led the annual product planning cycle, including strategic decision workshops to identify top priorities. Delivered an engineering-ready roadmap grounded in historical performance, heuristic analysis, and clear metric targets.
Last-mile food-access strategy for rural community impact: mapped pantry, food-bank, warehouse, volunteer, and retailer-partner variables; framed route optimization, pilot metrics, and buy-versus-build options; shaped a phased path from pilot to repeatable playbook and handoff.
Internal process automation across finance, legal, and HR — inbox triage, vendor and contract review, knowledge-base response — paired with metrics and trend analysis. Models compose and classify; source systems remain the factual layer.
Aggregated app-event and subscription data from multiple sources into a clean analytics dashboard, with automated weekly email reports surfacing retention, churn, and revenue trends — replacing a manual spreadsheet reporting process.
Built end-to-end docket notice PDF retriever with Sharepoint <> Filevine sync. Designed with SOC2 compliance in mind, including audit logging, automated webhook-driven Filevine folder reorganization, and aggregated email report updates.
Every engagement has a different shape. Sometimes the answer is a strategy doc. Sometimes it is a dashboard, an automation, an internal tool, a vendor decision, or an AI-assisted review loop. The unit of decision is the workflow: describe the work, decide whether to automate, build, buy, hire, or wait, then shape the system around the business.
The build-versus-buy conversation is really a workflow conversation. I do not start with a model, a vendor demo, or a dashboard. I start by naming the work clearly: what comes in, what good looks like, where the exceptions live, who owns the risk, and what has to happen after the output leaves the system.
Use AI where the pattern repeats, the edge cases are visible, and the result can be checked without turning the review into a second job.
Build when the shape of the work is specific to your business — your data, your rules, your approvals, your weird exceptions.
Buy when the tool already matches the way the work really happens, not just the clean version from the sales call.
Hire when the missing piece is judgment: someone who can define the work, own the standard, earn trust, or evaluate whether the system is actually good.
Wait when the category is moving too fast, the work is not described yet, or the team has better places to spend its change-management energy first.
Walkthrough rule: if we cannot explain the work plainly, we are not ready to automate it. First we map the workflow, standards, exceptions, owners, and success criteria. Then we decide whether the answer is build, buy, hire, wait, or automate.
Half the job is knowing what to buy, what to build, what to integrate, and what should just stay human. Below is the kit — the tools and patterns that keep showing up in the actual work.
“The answer is yes. The work is figuring out how.”
The model is not the system. The system is knowing what to automate, what to keep human, what data has to be deterministic, and how to make the whole thing safe enough for a team to trust on a Monday morning.