Data & Analytics · Build & Package
Describe the data. Get the spreadsheet.
Tell Doe "all enterprise customers who signed up this quarter with their usage metrics and contract values." It writes the SQL, pulls from your databases, joins the results in its sandbox, and delivers a clean spreadsheet. Every query is visible. Match rates are reported.
Works acrossNeonPlanetScaleGoogle Sheets
What you get.
Doe lets you describe a dataset in plain language and handles the rest: writes the SQL, pulls from your Postgres and MySQL databases, joins the results in a sandbox, and delivers a clean Google Sheets spreadsheet. Connects to Neon, Supabase, and PlanetScale. Every query is visible, match rates are reported for each join, and requests are saved as reusable templates.
Every stakeholder needs "just one quick data pull." You have twelve in the queue.
Sales needs enterprise customers with usage data by 3 PM. Marketing wants churned accounts with their last campaign touch. The CEO needs revenue by cohort and geography for a board slide. Each request is quick in isolation. Together, they're your entire week. And every one requires joining data that lives in different databases with different ID formats and a mapping table nobody documented.
The urgent ones get done with CSV exports and VLOOKUP, losing rows where IDs don't match across systems. The non-urgent ones sit in a Jira backlog growing faster than you can clear it. The analysis you were actually hired for gets pushed to "next sprint" indefinitely.
What changes.
- 01Request to deliveryBefore · Hours to days depending on analyst availabilityWith Doe · Minutes, depending on dataset complexity
- 02Repeat requestsBefore · Start from scratch ("can you rerun that with Q1 data?")With Doe · Saved as a template, rerun anytime with current data
- 03Cross-source joinsBefore · CSV exports and VLOOKUP, losing rows from ID mismatchesWith Doe · Doe pulls from each database and joins in its sandbox with match rates reported
- 04TraceabilityBefore · A spreadsheet with numbers and no record of how they were derivedWith Doe · Metadata tab with every query, match rate, and execution log
How Doe builds custom datasets
- 01Maps the plain-language request to tables and join keysDoeDoe resolves "enterprise customers with usage and contract data" to specific tables across your databases and generates a query plan you can review before execution.
- 02Pulls product usage metrics via PostgresNeonLogin frequency, feature adoption, and last-active timestamps staged for joining. SQL visible in the log.
- 03Pulls customer records and contract data via MySQLPlanetScaleDoe joins results from both databases in its sandbox. Match rate reported so you see how many records linked and which didn't.
- 04Writes the clean spreadsheet with metadataGoogle SheetsDoe delivers named columns, summary statistics, and a metadata tab documenting every query, execution time, and row counts at each join stage.
Up and running in under ten minutes.
- 01Connect your toolsOne-click OAuth for each integration. No API keys, no engineering.
- 02Describe what you need“Give me all enterprise accounts that signed up in Q4 with their monthly usage, contract value, and support ticket count. Export it as a Google Sheet with one row per account.”
- 03It runs on scheduleOn demand, or set a schedule so the dataset refreshes itself and lands in your spreadsheet.
Before you delegate.
- 01Can I review the SQL before Doe runs it?Yes. Doe generates a query plan mapping your request to specific tables and columns. You can review the SQL, edit it, or approve as-is before execution. Every query and its results are logged.
- 02How does joining across databases work?Doe pulls results from each database separately, then joins them in its sandbox using whatever shared keys exist: external IDs, email addresses, or mapping tables you define. Match rates are reported so you know how many records linked and which didn't. This is not a single SQL query across engines.
- 03Can I save a dataset request and rerun it later?Yes. Every request is saved as a reusable template. Rerun against current data anytime, or schedule recurring runs like "refresh this customer list every Monday."
- 04Does this work across different database types?Yes. Neon and Supabase (Postgres) and PlanetScale (MySQL) simultaneously. Doe handles SQL dialect differences automatically. Cross-database joins happen in the sandbox, not as a single query.
- 05Can non-technical team members request datasets?Yes. They describe what they need in plain language. Doe translates it to SQL, executes across your databases, and delivers a spreadsheet. The data team can review the generated queries if needed.