Positioning
Plenty of HR people use AI. I want to be the kind who can build the AI tools.
At CMoney Technology Co., Ltd., in an “HR Builder × PM” role, I took the high-frequency, repetitive, judgment-heavy work across recruiting, operations and analytics and re-architected it into systems, one at a time. The six flagship builds below are real, mostly live, and each comes with its architecture flow.
Stack: Python, FastMCP / MCP Protocol, LLM integration, DuckDB, Apache Parquet, PostgreSQL / SQL Server, Google OAuth / RBAC, Google Apps Script, GA4, Vibe Coding.
Flagship 1 · HR Data Lake
Query HR data in natural language: a data lake bridging SQL Server and an LLM.
Problem: HR data lived in two SQL databases. Every time a leader asked about “recruitment conversion” or “talent structure,” someone had to pull the data and build a report by hand. Slow, and no real-time decisions.
Architecture
Outcome: 5-tier OAuth role access, daily scheduled extraction, and the system auto-produces 3 email reports (CHRO daily situation room / data-lake health / data-access audit) plus 1 interactive decision dashboard.
Why it’s the flagship: it’s a full data architecture, running from data engineering (Parquet / DuckDB pipeline) through access governance (OAuth RBAC, five tiers) to LLM integration. It shows I can design a whole “HR data infrastructure” independently, end to end.
Flagship 2 · Résumé-screening agent
End-to-end résumé screening, with compliance governance and a human decision gate.
Pipeline
Governance by design: data-protection control points are built into the flow so résumé handling meets Taiwan’s PDPA; the AI only does an initial score, and whether a candidate advances, and the final hire, are the recruiter’s call. AI assists, and it can’t replace people.
Signal: most people building recruiting automation just want “fast.” I built PDPA compliance and a human decision gate in at the same time. That means what I weigh is the governance and risk of landing AI; efficiency is only one piece.
Flagship 3 · Seating & extension system (live)
Making seating data live and true: 593 seats · 457 people · 16 offices.
Problem: the old system was a hand-drawn seating chart in Google Sheets, with a separate Google Form for seat changes. The form was hard to parse and a hassle, so most people moved without filling it in; the chart drifted out of date and admin had to verify floor by floor.
Architecture
How it fixes it: drop a seat and the change is done, no form, and the person is notified automatically. Daily sync from HR master data: new hires land in “to be assigned,” leavers auto-release the seat and keep the extension, no manual upkeep. An SVG floor plan maps 1:1 to real desks; every change is logged.
Signal: the value here is fixing “data drift” at the source. What I saw was simple: if a seat move needs an extra step, people won’t do it. So I built the change into the move itself, one drag and drop and the record is right.
Flagship 4 · HR-ops email automation platform (live)
Full-lifecycle automation of 9 HR notification emails, with 7 security rules built in.
Problem: routine HR emails (onboarding, departure, unpaid-leave return, seniority/leave, etc.) ran on Excel + VBA scheduling (.xlsm): hard to maintain, no access control, no audit trail, hard to preview before sending, high misfire risk.
Architecture
Each email is a job directory (job.json defines subject / recipients / cron schedule + main.sql list query + template); the engine re-reads config on every run, so changes need no restart.
Safe-send pipeline
Outcome: 9 HR emails live, 3-tier role permissions, security rules S1–S7, 100% of write operations audited. Secrets only in environment variables, service bound to localhost, live send needs role permission + a one-time dry-run token + a confirm checkbox, the audit log is append-only (counts only, never list contents), and writes to the production DB are never automatic.
Engineering-grade governance signal: I built “HR operations” as software that fits pre-IPO internal-control principles, standing at the intersection of HR and security governance.
Flagship 5 · Tarot recruiting landing page
Creative × data × employer brand, in one build.
Concept: map 79 Osho Zen cards onto CMoney Technology Co., Ltd.’s three core values to build an interactive recruiting experience: candidates draw a card, get to know the culture, and the page does employer-brand communication and data collection at once.
Data design
Deployed on GitHub Pages, with GA4 tracking a four-stage conversion funnel and an integration with the 104 recruiting platform to close the loop. (A separate standalone version, the Breakdown Thinking Card, is also live.)
Signal: this build shows creativity (turning tarot into employer-brand narrative), data (GA4 funnel design) and delivery at once; at its core it turns “soft culture communication” into measurable conversion data.
Flagship 6 · Recruiting dashboards (funnel analysis × referral tracking)
Two independent single-file HTML dashboards, one for the conversion funnel on open applications, one comparing generations of the internal-referral program. Both ship as a plain file handed to HR and hiring managers: download it, open it, no server required.
Recruiting funnel & performance: pulls from the recruiting database’s eight core tables and mapping tables (applications, status workflow, job postings, departments, hire records), rolling apply → engage → screen → hire into one funnel, benchmarking conversion rate and time-to-fill side by side across departments and job postings, and cross-referencing view/apply counts from the 104 job-board back office as an external signal.
Architecture
How it works: every KPI carries a “data dictionary” tooltip. Hover it and you see exactly which table, which field, and which join produced that number, so a non-technical manager can verify it rather than trust a black box. The AI analysis runs as a two-stage conversation: the first pass produces insights, the second continues the same thread to generate action items, so long output never gets truncated; only aggregated statistics go to the model, never individual candidate data.
Referral-channel tracking dashboard: the internal-referral bonus scheme has gone through three rule generations, legacy, current, and a “D” variant. This dashboard puts all three generations’ referral records on one report, using hand-rolled stacked bars, a radar chart, pie charts, a monthly trend and a pivot heatmap to compare channels, tenure bands and generations on referral outcomes and retention.
How it works: it doesn’t lean on any charting library at all. Every stacked bar, radar, pie, monthly trend and heatmap is SVG written by hand. It also ships CSV import/export with a downloadable template, so records from different generations and sources can be pasted in and compared. A “data provenance” footnote spells out, in plain text, that “when the referral happened” and “start date” are two different time bases, so a reader doesn’t misread one as implying the other.
Outcome: neither dashboard is a templated shell. The funnel one labels every number with where in the database it came from; the referral one hand-builds its own chart engine. What I actually care about is whether a manager would trust the number enough to act on it.
Methodology
HR transformation should stop being a one-off project and become a capability that keeps running.
The value of this method is that HR transformation stops depending on a single spark of insight or personal heroics, and becomes a capability that’s repeatable, handoff-able and scalable. Leading a larger HR team, I’d make this the team’s standard operating process. It’s the core of my move from “someone who builds tools” to “a manager who builds capability.”