When the numbers don’t agree, I’m the one they call.

The pipeline has three defences. Schema contracts stop format changes before Bronze. Bad rows go to quarantine and can be replayed. A quality gate guards Gold. CI proves each one by injecting the exact failure it exists to catch. The analysis on top starts with an approved vendor list across eleven countries: 25 SKUs have no second source, and one origin carries 29.7% of COGS. OTIF splits into its two failures, and short-shipping, not lateness, is the bigger one. In the stock position, the shortfall grew 15.2% while total stock barely moved, because inventory kept flowing to the lanes that need it least. On the inbound side, 11,604 purchase-order lines score each supplier on delivery, quality, cost and responsiveness against published targets. 18 of 24 suppliers sit in a different band than their contract calls them. The contract tier is never an input to the score, so that gap is measured, not restated. Last, the textbook ABC service ladder, at the same spend, loses 1.72 points of unit fill, because it protects revenue, and a fill rate on units is not revenue.

One rule: no number without a query. The live demo needs no API key. A deterministic compiler matches your words to the warehouse’s columns, values and joins. On its 58-question test it gives 46 correct answers, 0 incorrect and 12 refusals. Add a key and a language model writes the SQL instead, behind the same access policy, SQL checker, read-only guard and row limits. A trust page brings the checks together: six versioned test suites, fifteen attack prompts across nine threat types, a sample four-role access matrix and five runtime budgets checked against the code. The test of the live model is deliberately left open, because it has not been run with a paid key. A business case covers stakeholders, options, requirements, UAT and rollout, and says where the demo stops and production approval would start.

The engine finds four kinds of mismatch between the ERP and the subledger, and traces every dollar back to source. It tests all 5,400 journal entries against controls, each measured on the entries where it should not fire. A certification step, GL-REC-01, then checks all 60 account-periods against a versioned 0.5% tolerance, open exceptions, how each was resolved and a 10-day deadline. It routes each one to a preparer and a reviewer by risk, and keeps those two roles apart. It hashes its evidence, and deliberately invalidates one earlier decision to prove that a stale approval gets caught. The result: 8 auto-certified, 9 prepared for review and 43 blocked. No human approval is faked.

Margin here is measured on pocket price: what is left after every discount, rebate, payment term, freight allowance and return. A margin quoted on list price lets a deal look healthy while it loses money. The output is a recommendation, not an elasticity: six ordered actions across 240 products, worth $2.65M of annual gross margin. Each says why it was chosen and how strong its evidence is.

A review log says who decides what. All 240 proposals are logged, and each material one goes to a named approver. 161 still wait on a human decision, and the release checks read 6 passed, 2 in review, 0 blocked. It then follows 2,075 approved past changes through fixed three-month before-and-after windows. 1,132 stay on a queue for explanation instead of being counted as a benefit. The monitor is labelled observational, and the public app cannot approve or publish a customer price.

What I would ask about first: the dataset is built so the analysis can be caught being wrong. Elasticity, cost pass-through and segment price sensitivity are planted on purpose, then recovered by the real code. The estimated category elasticities keep the planted order, at a rank correlation of 0.95. Every pass-through estimate comes out pulled toward zero, which is what a regression on noisy data should do. It took four attempts: when price moves only with time, it cannot be separated from the trend, and no elasticity can be recovered.

Underneath, the ERP data arrives the way ERP data does: SAP-shaped billing lines with missing standard costs, duplicate lines, unit-of-measure mismatches and orphan customers, all planted on purpose. All twelve quality rules find something. The reconciliation walks the extract down to the staged total, names every exclusion and leaves $0.00 unexplained. Six marts are computed a second time in DuckDB and held to the pandas versions at 1e-8. That is how a price-band median was caught five percent apart under the same column name.

The utility model uses EIA sales, twelve years of FERC Form 1 statements and NOAA weather. It splits a $100.9M plan miss into weather, price and usage, using Power Query, Power Pivot with DAX, and LINEST. A four-year backtest shows where the forecast stops being reliable. The same numbers publish in a seven-page Power BI report. A gradient-boosted load forecast is held to the same backtest: it helps where weather drives the load, and only a little. Kestrel Bay stays pinned to its tested release. It adds a distributor P&L, price-volume-mix, inventory ageing and channel economics.

Every B.C. municipality from 2019 to 2024, from the Province’s own filings. A pipeline downloads 90 files and holds each to a written contract, because the files change shape from year to year. Anything it has not seen before stops the run. A dbt project turns them into indicators, with 31 data tests. The Province’s annual replacement-need field is not usable as published: Vancouver reports 41% of its replacement value a year. So the model re-derives the need, and only where the reported value passes a screen. The pipeline also catches the Province’s own defects: a 2022 population file where 77 of 161 rows belong to other places, and municipalities with no statements. The Excel model covers any of 161 municipalities: peer benchmarks, renewal, a five-year plan, tax rates and borrowing room. For Fernie, closing the renewal gap by 2029 needs tax increases of 3.7% a year.

A 420-SKU, seven-site fulfillment network, generated the same way on every run, feeds six workspaces. Seven forecasting methods compete for each SKU in rolling backtests, and the results roll up to nine ABC-XYZ policy groups. A policy lab then tests limited shocks to demand, supplier lead time and service level, holding current stock fixed. It reprices safety stock, whole-case orders and lead-time exposure, and compares seven service targets on one yearly cost curve. Before suggesting a purchase, the app looks for transfers from surplus that pay for themselves. Supplier receipts, cycle counts, ageing and working capital end in one action list with six verbs. Fourteen tests hold the Python results to the DuckDB SQL marts, and uploaded workbooks stay in the visitor’s own session. The demo link is a static copy that opens at once; the full app, with uploads, takes about a minute to wake.

It began as a first-prize 2024 hackathon project that predicted yearly area burned. It is now a daily seven-day forecast built on real public records: 164,707 fires from the National Fire Database, 3,198 CWFIS weather stations and 4.0 million cell-days. The Canadian Fire Weather Index System is implemented from Van Wagner’s equations and checked against CWFIS’s published codes before anything relies on it. Two XGBoost models, a classifier and a Poisson count model, are averaged and calibrated. They train on 2000–2016, tune on 2017–2019 and are scored once on 2020–2024, against two baselines: each cell’s normal rate for the month, and an FWI regression. Re-ranked each morning, the riskiest tenth of cells held 57.5% of the 27,147 fires reported. Graded against this season’s satellite hotspots, it still beats FWI alone, but by less: 0.743 to 0.724. Those figures come after a fix. A pandas index misalignment had put 97.7% of fires in another fire’s cell, so the model had been trained and scored on a scrambled record. It showed up on a map, with Nova Scotia’s fires in the Yukon, and the README keeps the before and after. GitHub Actions rebuilds the forecast twice a day from live CWFIS and Open-Meteo data. An eight-page Power BI dashboard is generated from code and reconciled to the same evidence files. The original random forest is rebuilt too: its R² falls from 0.351 on a random split to 0.201 out of time.