Ihsan Wanda

Governing rule

The machine may propose; the domain owner disposes. Every AI-authored artifact is validated against source before it's trusted, and an owner can reject it. Of 135 proposed metrics, 128 were approved, 6 failed validation, and 1 was rejected by its owner — a metric tree with a 100% approval rate would mean nobody was checking.

Verified from the repository's validation report · owner decisions recorded separately and always take precedence

palm-analytics-dbt

Four keyless data sources → a Kimball star schema across 23 models (staging → intermediate → marts), with SCD2 snapshots, model contracts, custom macros, and 99 passing tests in CI. It serves an interactive dashboard where an estate manager decides the day's operations.

99 of 99 tests pass in CI · all four sources are public and keyless

developer-marketing-measurement-dbt

Marketing measurement where ad platforms over-claim conversions by hundreds of percent. Attribution triangulation: Multi-Touch Attribution + Media Mix Modeling (adstock & Hill saturation) + incrementality testing (geo-lift, holdouts), reconciled into one tested semantic layer. 15 models, 19 passing tests.

19 of 19 tests pass · campaign and PLG telemetry is synthetic; the modelling layer is real

CRM sales reconciliation — a manual process, rebuilt as a verifiable pipeline

Replaced a team's manual reconciliation with a pipeline that unified three separate systems, matching 18,537 orders (98.5%). Doing so surfaced defects the manual process had been silently absorbing:

Defect surfaced	Scale	Status
Records double-counted across two systems	12 records, 0.06% overstatement	fixed
Partially-paid records counted at full value	2.1% overstatement	fixed
Cancelled records with payment retained	87 records	escalated
Shipping counted as revenue	926 records	escalated
A "trusted" headline figure that moved 92% in one day	flagship metric	escalated

Each defect ships with a runnable verification query, so any claim can be re-tested instead of taken on trust. The point of the migration wasn't saving hours — it was that the numbers became verifiable.

Match rate measured on the reconciliation run · 4 of 5 defect classes were still open with the owning team at the time of writing

arc-cua — browser harness & grounded document extraction

An agent sees a page as a token-budgeted accessibility tree with [#N] action indices, and acts through verified actions. Built on it, ARC Index extracts each field from a document with an evidence quote and citation, and refuses any value it cannot prove at the cited row — then batch-fills the form and returns an audit receipt.

Measured on live cloud browsers	Baseline	This work
Task success, same model head-to-head	21 / 24	24 / 24
Input tokens across 24 runs	458,546	122,034 · 3.8× fewer
Input tokens per document appeal	26,955	1,209 · 22× fewer
Cost per appeal	$0.02575	$0.00305 · 8.4× cheaper
Wall time per appeal	89 s	11.4 s · 7.8× faster
Wrong values let through, of 60 fields	0	0 · equal accuracy

Same model on both sides. Raw per-run JSON is committed, and the measurement method is written up in the repository.

Runs recorded 2026-09-27 / 09-29 · token counts estimated from characters on both sides, so the comparison holds

sawit-field-crm

A field CRM app for palm-oil enumerators — Streamlit + Bitable API, 53 passing pytest tests. Building the tools the team actually uses is part of the job.

53 tests pass · single-team deployment, not a multi-tenant product

From industrial engineering → data → agentic AI

I started in industrial engineering, where the job was never to theorize — it was to build something, watch it fail under real conditions, and fix the flow. That's how I moved into data analytics about six years ago, and it's the same reason I'm moving into AI now.

My first AI-assisted build was a team donation app. The problem was ordinary and I'd seen it repeat: contributions announced in a chat group, transfer screenshots scattered across private threads, one person manually reconciling the mess before every deadline. The transparency was fine — what was missing was a clear flow. I volunteered to build it, shipped it, then ran it against real colleagues: ask for feedback, watch where the flow breaks, ship again.

That loop taught me more than any course, and it also exposed my own gap — I was copying code into a chat and pasting it back without understanding Git or CI/CD. So I rebuilt the loop around agentic coding, which let me read a pipeline instead of guessing at it. The projects above are the result of that shift.

129 of 129 tests pass · Supabase + Cloudflare Pages · donor sign-up, equal-split or custom amounts, combined payments, receipt upload, PIC verification, refunds on overshoot