Controls-first finance automation
Audit-ready finance automation.
Controls-first workflows in Alteryx, SQL, and Excel that reduce manual variance work, strengthen reliability, and accelerate close.
5 days faster post-close
30+ production workflows
2500+ annual hours saved
Close acceleration
Shortened post-close schedule by 5 days through standardized workflow sequencing.
Controls modernization
Rule-based evaluation with audit-ready validation and traceable exception handling.
Production ROI
30+ workflows in production, reducing approximately 2500 annual manual hours.
Foundations that don’t break
Repeatable ingestion + normalization patterns that produce decision-ready outputs you can actually trust.
Featured Demos
Featured Demo: Turning LLMs into a QA wingman for Alteryx
- Novel approach to process evaluation.
- Narrows the hunt to the most likely failure points (fast).
- Catches schema drift + join breakage before it becomes a production mystery.
Disclosure: Synthetic data and pseudocode only. No proprietary details.
Featured Demo: Have amazingly detailed process documentation? Let's try Context Engineering!
- Turns requirements into a modular workflow blueprint you can actually build from.
- Flags schema constraints early—before they become expensive rework.
- Author task documentation and executive notes in record time.
What I Build
Governed Data Foundations
- Ingestion to normalization to decision-ready outputs.
- Traceable transformations and consistent definitions.
Alteryx Automation Apps
- Standardized inputs, validations, and exception paths.
- Human-in-the-loop controls where required.
Finance-grade Pipelines
- Reconciliations, controls, reporting, and certification workflows.
- Deterministic, auditable, and maintainable by operating teams.
Case Studies
LLM-assisted QA triage for Alteryx workflows
Prompt-guided QA narrowed defect hunting to likely failure points and improved triage speed without exposing proprietary data.
Context Engineering for build-ready workflow specs
Structured context transformed process documentation into implementation-ready specs, reducing handoff friction and revision cycles.
How I Work
- I map the process like a system (inputs, outputs, constraints, and downstream dependencies).
- I surface assumptions early and go looking for the ways they can fail.
- I build deterministic workflows with explicit validations. Results are explainable, not magic.
- I document in business language with clean handoff artifacts. “Tribal knowledge” is not a control.
- I iterate with metrics, control testing, and feedback loops until it holds up under real pressure.
Controls are features
Exception paths matter
Documentation survives turnover
Model-agnostic prompts when AI is used
Lab Notes: controlled experiments in workflow automation
- Experiment: Can prompt-guided QA shrink defect triage time?
What happened: Triage got ~40% faster because the search space collapsed.
What I kept: A standardized issue taxonomy so the next run starts smarter.
- Experiment: Does “schema-first” spec work reduce handoff friction?
What happened: Fewer post-review revisions (less back-and-forth, fewer surprises).
What I kept: A required-fields checklist that prevents rework before it starts.
- Experiment: Can a “style bible” Markdown file can standardize documentation output (PowerPoints) without slowing delivery?
What happened: Decks became more consistent with fewer formatting passes and less “interpret the template” guesswork.
What I kept: A reusable Markdown style bible that drives layout, tone, and structure—so documentation stays on-brand and handoff-ready by default.
About
Hello. I’m Andy and I'd like to thank you for visiting my website.
I’m a self-proclaimed geek who gets way too excited about technology (especially AI) and what it can do when it’s applied with discipline. I build finance automation systems that hold up under controls, audits, and real operational pressure—usually across Alteryx, SQL, and Excel. I don’t automate broken processes, but I do love untangling messy ones: mapping cause-and-effect, stress-testing edge cases, and turning “tribal knowledge” into clean, handoff-ready artifacts. If your team needs reliable automation that actually gets adopted and measurably shortens cycle time, let’s talk.
