Services · Get your data into one place
“Our data is in five places and none of it matches.”
Data moved on time from where it is made to where it is used, cleaned and checked on the way.
- On this page
- Live cleaner, live
- Fixed-price start
- Data Dashboard in 7 Days · ₹40,000 or $1,200
Draft price These prices are drafts, not final. Nothing is charged without a written quote that you accept first. Prices in dollars are for clients outside India.
You need it when
- Reports break whenever a column moves.
- The same customer appears under three spellings.
- You want AI to answer questions about your data, but the data is a mess.
- Nobody is sure which file is the real one.
Messy rows in. Clean rows out. Nothing guessed.
Nine customer rows the way they arrive from three different systems. Switch the cleaning steps on one by one, or run them all, and watch every cell that changes. Rows that fail a check go to a person.
Cleaning steps
- Rows in
- 9
- Clean rows out
- 9
- Cells changed
- 0
- Duplicates
- 0
- For a person
- 0
| # | Name | Phone | City | Date | Amount | Note |
|---|---|---|---|---|---|---|
| 1 | rahul MEHTA | 98100 12345 | delhi | 12/03/2026 | ₹1,200 | |
| 2 | Anita Rao | +91-98765-43210 | Bengaluru | 2026-03-12 | 1200.00 | |
| 3 | Rahul Mehta | 9810012345 | Delhi | 12 Mar 2026 | 1,200 | |
| 4 | S. Iyer | 044 2345 6789 | chennai | 13/03/2026 | ₹ 850 | |
| 5 | Farah Khan | 98765-11111 | Mumbai | 2026/03/14 | 2,450.5 | |
| 6 | VIKRAM SINGH | 9988776655 | Jaipur | 14-03-2026 | TBD | |
| 7 | anita rao | 9876543210 | Bangalore | 12.03.2026 | ₹1200 | |
| 8 | meera nair | 98470 11223 | Kochi | 31/02/2026 | 600 | |
| 9 | Arjun Das | 9433 012 345 | kolkata | 15 Mar 2026 | 3,075 |
For a person · 0
Rows that fail a check land here with the reason. Nothing is guessed.
Turn on "Show what changed" to see the old value next to the new one. All rows are example data.
What you get.
Ingestion from your sources
APIs, exports, shared folders and email attachments, brought in on a schedule.
Cleaning rules, written down
Each rule is a named, tested step: one date format, amounts as numbers, one spelling per city.
Checks that stop bad rows
A row that fails a check is set aside for a person, with the reason, instead of being silently fixed or dropped.
A database built to be queried
Postgres tables designed for the questions you ask, ready for a dashboard or an AI assistant.
A history of changes
What changed, when, and by which rule, so any number can be traced back to the row it came from.
In plain words, what it runs on, and who owns it
Getting your data into one place is the plumbing under every dashboard and every AI assistant. Data is made in many places and in many shapes: dates written three ways, the same customer twice, amounts with and without the rupee sign.
We build pipelines that bring it in on time, clean it the same way on every run, check it, and keep a history of what changed. Rows that fail a check go to a person instead of being guessed.
What it runs on
Scheduled pipelines from your sources into Postgres tables designed for your questions, with a written history of every change.
Who owns it at handover
Who owns what (the code, the data and the accounts) is written into the scope before we build anything, so you know before you pay. At handover you get a walkthrough of how it works and what to do when it stops.