Not just the answer. The path to every number.
Several tables on one sheet, subtotal rows, decorative headers, inconsistent spellings. Spreadsheets made for people to read — D2B takes them as they are and delivers reconciliations, summaries, and reports. Every number is traceable line by line, so you can submit to finance or your manager with full confidence.
| Date | product_id | Region | qty | Sales |
|---|---|---|---|---|
| 3/1/2026 | SKU-447 | seattle | 12 | $48,000 |
| 2026-03-02 | sku-101 | SEATTLE | ∅ | 32k |
| Mar 3, 2026 | SKU-204 | boston | 7 | $21000 |
| ∅ | SKU-447 | Seattle | NA | — |
| 3/4/2026 | 101 | austin | 5 | 15,000 USD |
| 3/5 | sku-318 | ∅ | 8 | $40,000 |
| 2026-03-06 | SKU-512 | boston | 11 | 33k |
| ⋯ | ||||
| 3/9/2026 | SKU-447 | Boston | 9 | $27,000 |
| 2026.3.10 | sku-204 | SEATTLE | 6 | 18000 |
| March 12 | SKU-101 | Austin | 4 | $12,000 |
| 2026-03-14 | SKU-318 | ∅ | NA | — |
| 3/18/2026 | sku-512 | SEATTLE | 20 | 80k |
| ∅ | 447 | boston | 13 | 39,000 USD |
| ⋯ | ||||
| 2026-04-22 | SKU-447 | boston | 15 | $45,000 |
| 4/30/2026 | SKU-204 | AUSTIN | 8 | 24k |
14 products declined >15% quarter-over-quarter.
| date | product_id | region | qty | sales_jpy |
|---|---|---|---|---|
| 2026-03-01 | SKU-447 | TOKYO | 12 | 48,000 |
| 2026-03-02 | SKU-101 | TOKYO | 8 | 32,000 |
| 2026-03-03 | SKU-204 | OSAKA | 7 | 21,000 |
| 2026-03-04 | SKU-101 | KYOTO | 5 | 15,000 |
| 2026-03-05 | SKU-318 | OSAKA | 8 | 40,000 |
| 2026-03-06 | SKU-512 | OSAKA | 11 | 33,000 |
Nulls, mismatched headers, mixed formats, multi-sheet — absorbed inside the agent.
"What dropped MoM?" "Who's at risk?" — questions stay questions.
LLM-generated spreadsheets give you the values but no working — there's no way to verify them. D2B records every cleaning, joining, and aggregation step, so every output number traces back to the input rows it came from. Review the path itself instead of rebuilding the math.
Managers, finance, sales, planning — each asks its own question of the same sales data and gets its own deliverables back.
Which team members are at risk of missing this quarter's target?
Roll up 10 department feeds into a single monthly statement
6-month sales trend by rep × region × product
One-page monthly board summary
Chart, table, and report — each downloadable in the formats your existing tools already speak. Every step is visualised as a DAG; save the whole sequence as a workflow with one click.
Comparison, trend, distribution — the agent picks the right shape and returns an interactive Plotly chart. Export as image or JSON to drop straight into your slides.
A cleaned, joined, aggregated analytics table — columns in the order you'd want. Download to Excel or CSV and feed it into Sheets, your BI tool, or downstream systems.
Summary, drivers, recommendations — structured for use. Pick PDF for the board, Word or Markdown for internal share, HTML for web distribution.
Cleaning, joining, aggregation — each step persists as a node with explicit dependencies. Every number traces back to the input row it came from. Review-ready, audit-ready, and the basis for trusting the output.
Capture the full sequence — question to output — and replay it next week, next month, or every morning. Recurring work runs the same way every time, without depending on one person's memory.
You'll see the answer before you finish cleaning it.