# Desk log ADR-XAE-20260825

**Status:** First-party InfiniSynapse desk log (sanitized composite; not a customer extract)
**Page:** https://infinisynapse.com/en/blog/explainable-ai-examples
**Run ID:** `ADR-XAE-20260825`
**Date:** 2026-08-25 (Tuesday)
**Last verified:** 2026-08-29
**Operator:** InfiniSynapse Data Team
**Attestor:** William Zhu, InfiniSynapse cofounder ([GitHub @allwefantasy](https://github.com/allwefantasy); no personal LinkedIn)
**Contact for contradictions:** zhuhl@infinisynapse.com

## What this file is

A downloadable record of answering a monthly refund-rate question with an unlabeled “mix” pack, then opening the labeled remap pair that still held both grains and the collision note. It is **not** a named-logo customer case, a vendor bake-off, or a Fiscal Data / IMF / Stanford / McKinsey experiment.

Companion files: [aggregate CSV](https://infinisynapse.com/blog-media/explainable-ai-examples/downloads/aggregate-ADR-XAE-20260825.csv) · [verify script](https://infinisynapse.com/blog-media/explainable-ai-examples/downloads/verify-ADR-XAE-20260825.py).

## Four-step method (reproducible)

1. Bind a monthly operations-adjacent goal on a read-only orders source plus a one-page remap note locking SKU family. Sanitize first.
2. Request a Markdown memo, two charts, and intermediate tables for grain A (month × shipped order) and grain B (month × SKU family).
3. Accept the first “mix” pack as the control. Score three checks: grain A named, grain B named, collision labeled.
4. Open the labeled remap pair and score the same three checks. Keep first and second artifacts side by side.

## Source and goal

| Field | Value |
|---|---|
| Sources | One orders table, about 12,685 fulfilled lines across two complete months, plus a one-page remap note locking SKU family |
| Standing goal | Why did refund rate move last month versus the prior month |
| Contrast | Unlabeled “mix” pack vs labeled remap pair |

## Results

| Retrieval state | Grain A named | Grain B named | Collision labeled |
|---|---|---|---|
| Unlabeled “mix” pack | 0 | 0 | 0 |
| Labeled remap pair | 1 | 1 | 1 |

Fulfilled rows on this run: 6,275 (prior month) vs 6,410 (later month). The unlabeled pack said “mix.” The labeled pair named the collision: one SKU remapped mid-month and landed in a different family. The reviewer rejected the first pack and accepted the second.

Wall-clock for the successful pair: 10 minutes (warehouse time excluded). The clock started when the operator opened the standing goal and ended when both grains, the remap label, and the files sat in one folder.

## What you may cite

- Artifact counts 0/0/0 → 1/1/1, 6,275 vs 6,410 fulfilled rows, ~12,685 lines, ~10 min wall-clock, run ID

## What you may not claim

- Customer uplift %, official EEAT score, named-logo case, or that Fiscal Data / IMF / WHO / NLM / ICH / Stanford / McKinsey / Gartner / NIST / OWASP / WAIC / DataCite / W3C DCAT scored this run

## Independent context (not this run)

Retrieved 2026-08-29. None of these sources evaluated this page.

- [Fiscal Data](https://fiscaldata.treasury.gov/)
- [IMF Data](https://www.imf.org/en/Data)
- [WHO Data](https://www.who.int/data)
- [National Library of Medicine](https://www.nlm.nih.gov/)
- [ICH](https://www.ich.org/)
- [Stanford HAI AI Index](https://hai.stanford.edu/ai-index)
- [McKinsey State of AI](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)
- [Gartner Peer Insights — Analytics and BI Platforms](https://www.gartner.com/reviews/market/analytics-business-intelligence-platforms)
- [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework)
- [OWASP Top 10 for LLM Applications](https://owasp.org/www-project-top-10-for-large-language-model-applications/)
- [W3C DCAT](https://www.w3.org/TR/vocab-dcat-3/)
- [DataCite](https://datacite.org/)
- First-party homepage recognition only (self-described; not independently verified on this page): [2026 WAIC Future Tech OPC Excellence Award](https://infinisynapse.com/#recognition)