Datumm
Early access open

Messy data.
Plain English.
Done.

Describe the transformation. The agent writes the code, runs it, and tells you exactly what changed — before anything moves forward.

Try the product →
datumm.vercel.app
"Normalize emails to lowercase. Remove rows where email is missing."
running transformation…
What changed
843 rows removed — email missing or invalid.
49,157 rows remain. Email column: all lowercase.
49,157 kept
843 removed
100% normalized
The Problem

Data work is broken
at the tooling layer.

The messy file
Emails in every case. Dates in three formats. 800 duplicates. You know what to do — getting there costs two hours or a ticket that won't ship until Thursday.
The copy-paste loop
Ask Claude. Copy code. It breaks. Paste error. Repeat. 11 iterations. 90 minutes. The transformation: 3 seconds.
The black box
No errors. You spot-check 10 rows and hope. Three weeks later, someone finds the dedup kept the wrong records.

This isn't a skills problem.
It's a tooling problem.
The work hasn't changed. The tools haven't either.

Tooling gap
The Product

This is what
tooling catching up looks like.

Drop a file. Describe what you want. Confirm what changed. Repeat.

datumm.vercel.app
Datumm product screenshot
The Mechanism

Three steps.
Every time.

01
Describe

Drop a file. Add a Transform block. Write what you want in plain English.

"Normalize emails to lowercase and remove rows where email is missing."
02
Execute

The agent reads your data, writes pandas code, runs it in a sandbox. Retries on failure.

03
Verify

A card shows what changed. Confirm or describe what's wrong. The agent corrects and reruns.

When something's wrong, you describe it. "Flag them instead of removing." The agent rewrites, reruns, shows you the result.

The Direction

The agent learns
your data.

Every correction compounds.

In six months, Datumm remembers every rule. In a year, it applies them before you ask.

"Don't remove missing emails — flag them."
"Revenue column is always in cents from this source."
"Dedup on order_id, not row_id."
Avg. cleanup time
2.5 hrs
With Datumm
3 sec
Copy-paste loops
11×
With Datumm
1×
No Overselling

Here's where
Datumm is right now.

Works today
  • CSV and Excel files
  • Python (pandas) code generation
  • Isolated sandbox execution
  • Plain-English validation cards
  • Step-by-step confirmation flow
On the roadmap
  • Team workspaces
  • Database connectors
  • Scheduled pipelines
  • Cloud integrations

If you have a messy file and you know what you want it to look like, Datumm handles that.

We're building this in public. If you join the waitlist, you're not waiting for a finished product. You're shaping one.

Join the waitlist.
Shape what's built next.

Early access is free. First users get direct input on the roadmap.

No spam. No sales calls. Direct line to the founders.