A complete browser-based workspace for turning scattered files into clean, connected, analysis-ready data. Filter it, join it, calculate on it, ask an AI for the SQL, and check it with diagnostics - all saved in one project you can reopen and refresh.
Any source, one workspace
Import Excel, CSV, or database tables side by side - no reformatting before you can start.
Visual + SQL, your call
Click through a transformation step by step, or drop into SQL directly - switch between them on the same pipeline.
Diagnosed, not guessed
Column-level checks catch type mismatches and nulls before export or Odoo Sync, not after your team finds them.
Stack AND / OR conditions, date ranges, null checks, and text-pattern matching into named filter sets you can reuse across a project. Every rule stays visible in the step list, so anyone reopening the project can see exactly what was kept and what was dropped.
Technical DetailExample: keep only PIM-published products, or isolate one brand's rows without touching the source file.
Pick two tables, choose the matching field, and merge with a left, right, inner, or full join. Before you commit, Analysis Studio previews unmatched rows and flags duplicate keys, so a bad join never quietly breaks your output.
Technical DetailExample: attach brand and family lookups to a raw product export in one governed step.
Rename, reorder, hide, split, or re-cast a column's type from one panel. Each field is profiled for nulls, distinct values, and type drift as you work, so a stray blank or mismatched type shows up before it reaches an export.
Technical DetailExample: hide temporary working columns like quality_type while keeping SKU and brand locked for downstream tools.
Build a calculated column with conditions, text functions, date math, or arithmetic, and test it against a sample of rows before it runs on the full table. Formulas stay editable, so a rule you set today can be tuned next month without redoing the column.
Technical DetailExample: derive a margin band, a normalized SKU, or a campaign period straight from existing fields.
Type what you want to know in plain language and the AI SQL Assistant writes explainable DuckDB SQL matched to the tables and columns already in your project. Review it, edit it, and save it as a repeatable step instead of a one-off query.
Technical DetailExample: ask "show brands with the highest return rate this quarter, excluding incomplete orders."
A dedicated diagnostics panel checks every column for nulls, duplicate keys, unexpected values, and type conflicts as you shape the data. Issues surface next to the step that caused them, not buried at the end of the pipeline.
Technical DetailExample: get flagged the moment a merge introduces a duplicate SKU, before it ships downstream.
Save a shaped dataset as a template so next month's file goes through the same filters, joins, and calculated columns automatically. When the destination is Odoo, sync the finished table directly instead of exporting and re-importing by hand.
Technical DetailExample: apply last month's product-cleanup template to a fresh export in one click.
Stop rebuilding reports. A Supabind project saves every filter, join, and formula as a repeatable workflow. Go from messy files to a clean, trusted dataset you can refresh with one click.
Add CSV, Excel, or extracted PDF tables. Analysis Studio profiles types, rows, and key fields as they arrive, and lists every table you add in the Tables panel.
Filter, merge, manage columns, and add calculated fields in a visual sequence. Each step remains editable and the source file stays untouched.
Run the diagnostics panel to catch nulls, duplicate keys, unexpected values, and type conflicts before you export or sync anything downstream.
Keep the shape, the SQL, and the output rules together as a project or template. Refresh it with next month's source, or sync straight to Odoo, without rebuilding a thing.
Bring in the source, build a trusted transformation flow with Filters, Merge, Columns, and the AI SQL Assistant, run Diagnostics, and return to it whenever the data changes.
View pricingNo. Ask a question in plain language and it writes the SQL behind the scenes. If you already know SQL, you can read, edit, and rerun every query it generates instead of clicking through steps.
Excel and CSV files today, plus tables pulled from database connections. Everything lands in the same workspace, so you're not switching tools between import and analysis.
Every column gets checked for type mismatches, nulls, and obvious formatting issues before the results leave the workspace - so problems get flagged before export or Odoo Sync, not after.
Yes. Save any sequence of cleanup and transformation steps as a workflow, then run it again on a new file in one click without rebuilding it.
Yes. Once a dataset is cleaned and validated, you can push it straight to Odoo through Odoo Sync instead of exporting and re-importing manually.