Intercompany reconciliation 平台 — 使用合成資料的工作示範。七個頁面、六次月度關帳,完整向下鑽取至帳目條目。
AI 翻譯
Intercompany reconciliation platform — working demo with synthetic data. Seven pages, six monthly closes, full drill-down to the ledger entry.
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一分鐘完成驗證正在載入完整 README。 以下為已儲存的摘錄。
A reconciliation platform for a multi-entity group: it takes the general ledger
and the delivery system, rebuilds every intercompany relationship, and shows
where the two sides of the group disagree — down to the journal entry.
All data on this site is synthetic. Every company, project, client,
person and amount was generated for this demo. The model is real: the
figures reconcile, the controls run, and every drill-down ties back to the
ledger line it came from.
Seven pages over six monthly closes, all anchored to the same cut:檢視完整 README ↗A reconciliation platform for a multi-entity group: it takes the general ledger and the delivery system, rebuilds every intercompany relationship, and shows where the two sides of the group disagree — down to the journal entry.
All data on this site is synthetic. Every company, project, client, person and amount was generated for this demo. The model is real: the figures reconcile, the controls run, and every drill-down ties back to the ledger line it came from.
Seven pages over six monthly closes, all anchored to the same cut:
| Page | What it answers |
|---|---|
| Overview | Where does the group stand this close, across all three domains |
| AR / AP | Which intercompany balances disagree, by pair, entity and document |
| Loans | Which loan pairs do not mirror, and which entity books only one side |
| Revenue → Cost | The full cycle in ten perspectives: recognition, invoicing, WIP, cost |
| Elimination | Whether the consolidation entity absorbed everything measurable |
| Controls | The control panel: what was measured, what was expected, what was found |
| Help | The vocabulary, so the numbers can be read without a hand-over |
The dataset is generated, not hand-written: a synthetic general ledger is
posted entry by entry, and all 19 datasets the report reads are derived from it.
That is why the tie-outs hold — they cannot do otherwise. The generator is in
generator/, with its own notes.
6 monthly closes · ~2,500 journal entries · 19 datasets · 41 controls
It is a static site — no server, no build step, no database.
python3 -m http.server 8000
Then open http://localhost:8000.
Built by Engels Alvarez · [email protected]