# Desk log ADR-XAI-20260825

**Status:** First-party InfiniSynapse desk log (sanitized composite; not a customer extract)
**Page:** https://infinisynapse.com/en/blog/explainable-ai-data-analysis-guide
**Run ID:** `ADR-XAI-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 contribution-margin question with a paragraph-only memo, then opening the task trail that still held the plan, the SQL, and the mix table. It is **not** a named-logo customer case, a vendor bake-off, or an OWASP / Stanford / McKinsey experiment.

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

## Four-step method (reproducible)

1. Bind a monthly operations-adjacent goal on a read-only orders source plus a cost extract and a one-page definition note locking “contribution margin” (shipping passthrough excluded). Sanitize first.
2. Request a Markdown memo, two charts, and an intermediate mix table by SKU group.
3. Accept the first fluent paragraph as the control. Score three checks: plan names grain, SQL openable, mix table visible.
4. Open the finished task trail and score the same three checks. Keep first and second artifacts side by side.

## Source and goal

| Field | Value |
|---|---|
| Sources | One orders table plus a cost extract, about 8,305 fulfilled lines across two complete months, plus a one-page definition note locking “contribution margin” |
| Standing goal | Why did contribution margin move last month versus the prior month, fulfilled orders only |
| Contrast | Paragraph-only memo vs reopenable task trail |

## Results

| Retrieval state | Plan names grain | SQL openable | Mix table visible |
|---|---|---|---|
| Paragraph-only memo | 0 | 0 | 0 |
| Reopenable task trail | 1 | 1 | 1 |

Fulfilled rows on this run: 4,085 (prior month) vs 4,220 (later month). The first paragraph claimed a 1.8 point mix shift. The mix table showed one SKU group was a new bundle with a different cost key. The reviewer rejected the first paragraph and accepted the restated plan.

Wall-clock for the successful trail: 12 minutes (warehouse time excluded). The clock started when the operator opened the standing goal and ended when the mix table, the SQL, and the memo sat in one folder.

A separate filter check on the same source showed 12,481 order lines becoming 11,902 after the standing exclusion. That count is this run only.

## What you may cite

- Artifact counts 0/0/0 → 1/1/1, 4,085 vs 4,220 fulfilled rows, ~8,305 lines, ~12 min wall-clock, run ID

## What you may not claim

- Customer uplift %, official EEAT score, named-logo case, or that OWASP / EU / Stanford / CISA / McKinsey / Gartner / ISO / NIST / ENISA / Wikipedia / WAIC / DataCite / W3C DCAT scored this run

## Independent context (not this run)

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

- [OWASP Top 10 for LLM Applications](https://owasp.org/www-project-top-10-for-large-language-model-applications/)
- [European Commission: approach to AI](https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence)
- [Stanford HAI AI Index](https://hai.stanford.edu/ai-index)
- [CISA: Artificial intelligence](https://www.cisa.gov/ai)
- [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)
- [Wikipedia explainable AI overview](https://en.wikipedia.org/wiki/Explainable_artificial_intelligence)
- [ISO/IEC 42001](https://www.iso.org/standard/81230.html)
- [NIST SP 800-53](https://csrc.nist.gov/pubs/sp/800-53/r5/final)
- [ENISA AI cybersecurity framework](https://www.enisa.europa.eu/publications/multilayer-framework-for-good-cybersecurity-practices-for-ai)
- [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework)
- [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)