$60.2M
spend modeled
Operations · Data governance · Reliable automation
I turn complex systems into trusted ones.
I design auditable workflows, governed data systems, and reliable automation that help teams make clearer decisions, move faster, and deliver dependable outcomes.
10+ years improving production operations, media systems, and information management
10+years across data & operations
Built for confident change
01 · Measurable impact
A traceable model brought $60.2M into one reviewable view.
In an anonymized case analysis, I translated business rules into a formula-driven model that assessed 54 projects and surfaced $20.2M in potential opportunities for review.
$20.2M
potential opportunities surfaced
33.6%
modeled opportunity rate
54
projects analyzed
Anonymized case analysisIllustrative opportunity scopeFormula-drivenReview controls included
02 · Selected systems
Public work. Clear value.
Three public projects. Each shows how I turn complex requirements into usable tools, visible controls, and verifiable results.
BOTOPS / BROWSER DEMO
Ready
Systems overviewReady for review
Simulated, project-scoped operations reviewLauncher auditReady
Health evidenceReady
Support exportReady
Browser-only sample
Python 3.10+ · Standard library · Windows
Ready to review three simulated operational signals.
01
Operations engineering · Python
BotOps Manager
A local Windows operations console that brings independently launched automation projects into one observable workspace through launcher audits, structured health evidence, scoped controls, and privacy-conscious support exports.
Open case note
- Opportunity
- Give operators one clear view of independently launched automation while preserving project-level boundaries.
- Approach
- Combine launcher audits, identity-aware process ownership, structured health contracts, and bounded support exports.
- Value
- Improves operational visibility and supportability while keeping actions explicit, verified, and project-scoped.
- Evidence
- The public Python 3.10+ implementation uses only the standard library and includes deterministic tests built around synthetic projects and mocked process inventories.
Runs locally with Python 3.10+ and the standard library, making it portable across Windows workstations without additional services or installers.
Related implementation: Avalon Q Supervisor applies the same evidence-first operating model to a local edge device.
GOV_AUDIT / 018 assets
0.74mean confidence
02
Data governance · Audit
Digital Asset Governance Audit
A repeatable governance audit that validates records, measures provenance and integrity coverage, and prioritizes evidence-backed human review.
Open case note
- Opportunity
- Turn mixed asset inventories into clear priorities for provenance, integrity, and review.
- Approach
- A deterministic Python audit measures evidence coverage and prioritizes review.
- Value
- Source-preserving analysis gives reviewers clear, evidence-ranked priorities.
- Evidence
- 8 / 8 synthetic records validated; source-reference and review-date coverage measured at 100%.
Results reflect a controlled eight-record fixture.
03
Metadata · Reliability
MediaTaggerBot
A media-matching workflow that combines ranked evidence, guided preview, verified updates, and a complete change history.
Open case note
- Opportunity
- Help media teams make fast, confident identity matches with a transparent evidence trail.
- Approach
- Ranked evidence, guided preview, a SQLite journal, and clear diagnostics.
- Value
- Each update meets a defined confidence standard, then is verified and recorded.
- Evidence
- Public code documents the decision standards, verification path, and complete change history.
03 · Operating model
Build confidence from intake to outcome.
Reliable automation connects clear standards, visible signals, deliberate validation, and operational continuity.
Explore the operating model
Confidence at
every step.
01 / Clear rules before automation
Set standards for provenance, required fields, confidence, and review ownership before work begins.
Operational valueClear inputs move through consistent, reviewable decisions.
Working principles: governed, observable, adaptable, and human-centered.
04 · Experience
Builder range.
Operator discipline.
My career began in film and post-production, where quality depends on coordinated teams, precise naming, disciplined handoffs, and continuity. I now apply that same rigor to data governance, reporting, workflow design, and information systems.
13editors supported through real-time metadata
10contractors coordinated across operations
2018 — Now
Gateway Information Group
Advising teams on information management, workflow improvement, governance, and operational documentation.
2022 — 2025
Visa · Production Operations
Built production database structures, automated progress and spend reporting, and strengthened taxonomy, metadata, and responsible technology adoption.
Earlier
APR · Digital Archive Group · Harpo Studios
Delivered financial and asset reporting, archive migrations, database operations, metadata management, and cross-functional project coordination.
05 · Foundation
Creative roots.
Information rigor.
A career spanning the information lifecycle—from creation and production to organization, governance, and experience.
2019
M.S. Information Management
Dominican University
2019
Certificate in Digital Curation
Dominican University
2006
B.A. Film / Video
Post-Production Management · Columbia College Chicago
A small gift for curious builders
Two bots. One workflow.
Built to work as a pair: the 10×1¢ bot plans tightly scoped buys, while the 15-minute sell bot reads existing positions and previews sell-side exits.
01 / BUY
Kalshi 10×1¢
Dry-run first, with tightly scoped order capability available only after explicit verification and activation gates.
Get the buy bot ↗ 02 / SELL Kalshi 15m SellA read-only dry-run preview for exploring sell-side planning with existing positions and no order submission.
Get the sell bot ↗MIT-licensed learning releases. Verify before use, begin with Kalshi’s demo environment, and review current eligibility and platform terms. Event contracts can lose their full principal. Independent projects; not affiliated with or endorsed by Kalshi.
06 · Start a conversation
Make the next system easier to trust.
Let’s make your next workflow clearer, more reliable, and ready to scale.
jerryrnapier@gmail.com