Getting the data out
We pull it from your ERP, CRM, website, billing system and the spreadsheets nobody admits to — overnight, or the moment something changes.
Data Engineering
Get your numbers to agree with each other.
Sales says one figure, finance says another, and every report starts with three days of cleaning up spreadsheets. We fix that underneath — pulling the data out of every system you run, making the names and dates line up, checking it automatically, and keeping it arriving on time. After that, reports and AI stop being a project each time.
In plain terms
One invoice.
Five different answers.
This is the whole problem in one picture. The same sale exists in five systems, and no two of them agree on the customer’s name, the date or the amount. Nobody is wrong — each system is recording it the way it was built to. The work is deciding, once and in writing, which version is the truth, and then making that happen every night without anybody watching.
That is data engineering. Not a dashboard, not a model — the agreement underneath both of them.
Get your sources audited →SystemCustomerDate
Amount
ERP
COMPANY 1 LTD
12/09/26
₹4,18,600
CRM
Company 1 Ltd.
09-12-2026
418600
reads as 9 Dec
Shop
company 1 limited
2026-09-12
418,600.00
Finance
COMPANY 1 (Pune)
12 Sep
4.19 L
rounded
Courier
COMPANY 1 LTD.
12/09
—
value missing
- Trading names matched to one customer id
- Dates parsed with each system’s own format
- Amounts normalised to paise, not lakhs
- The missing value filled from the invoice, not guessed
One agreed row
CustomerCompany 1 · C‑1187
Date2026‑09‑12
Amount₹4,18,600
5 sources reconciled · 142 checks passed
Sample values, real mismatches — every one of these five is a disagreement we have had to settle on a live project.
Capabilities
What exactly we do
Six jobs, none of which anybody sees — and all of which decide whether the number in your report can be trusted.
One place to keep it
A single store your reports and your team read from, instead of eight exports that disagree. Postgres, BigQuery or Snowflake, sized to what you actually ask it.
Making it agree
Names, dates and amounts from different systems turned into one version. The rules are written down as code, so anyone can see why a number is what it is.
Checking it before you see it
Every load is tested: is it late, is it short, are there duplicates, are there blanks. A bad load stops instead of quietly skewing the board pack.
Keeping it running
It runs on its own overnight. If a step fails it retries, and if it still fails it wakes one of us — rather than you, in a meeting, three days later.
Showing where a number came from
Any figure can be walked back to the record it started as, through every step that touched it. Useful in an audit, and decisive in an argument.
What changes
Four things that stop being anyone’s job
Data engineering is hard to sell because none of it is visible. These are the four changes people actually notice in the month after it lands.
The monthly clean-up before anyone can report
The export-join-check ritual at the start of every reporting cycle stops happening, because the joining and the checking already ran last night.
Sales and finance quoting the same figure
Every metric is defined once, in code, and reviewed like code. Revenue means one thing in the board pack and in the sales dashboard.
How stale the numbers are allowed to get
Nightly for most things, every fifteen minutes for the handful where a decision cannot wait — decided per table, with you, rather than everywhere at once.
Every later report and AI feature reuses it
The warehouse is the thing the next five projects stand on. It is why an agent that took six months at the first attempt takes six weeks at the second.
Overnight
What happens to one sale, overnight
Five steps between somebody entering an order in your ERP and somebody else trusting the number in a report.
-
ArriveCopied out on a schedule, or the moment it changes, and kept exactly as it arrived.
-
TidyNames, dates and amounts lined up, and joined to the other systems holding the same sale.
-
CheckLate? Short? Duplicated? Blank? Anything that fails the checks stops right here.
-
ShareHanded to your dashboards, to your analysts, and to anything an AI assistant reads from.
-
RememberThe trail is kept, so any number can be walked back to where it started.
If a load fails it stops and raises an alert, rather than publishing half a day of data and letting somebody find out in a meeting.
How we deliver
What you get, every time
A data platform is only worth having if people trust it, and trust comes from tests rather than from assurances.
Talk to an engineer- One organised store, not a folder of exports
- Automatic checks for data that is late, short or duplicated
- Everything in your own repository, where your team can read it
- People see only the rows they are allowed to see
- It runs in your cloud account, set up so it can be rebuilt exactly
- A written runbook and a recorded handover session
Next door
Already have the pipelines?
Data engineering is the foundation. What you build on top of it depends on whether you need answers or visibility.
Our process
How we work
From four systems that disagree to one set of numbers nobody argues with.
Proposal sign-up
Scope, timeline and a fixed number in writing — agreed and signed before any build starts, so nothing moves later without a conversation.
Strategy & planning
We list every system, who owns it, and what each field is supposed to mean — including the ones that contradict each other.
Design & development
We agree what each number means and how the store should be shaped, with the people who actually use it.
Testing & deployment
Built in slices, so something useful is working in week three rather than in month four.
Support & maintenance
We watch it, keep the cloud bill sensible, and review each quarter what is still being used.
Before you ask
Questions we get asked
What is the difference between data engineering and analytics?
Data engineering builds the pipelines, warehouse and tests that make the numbers trustworthy. Analytics is the work of interrogating those numbers to answer a business question. You need the first before the second is worth paying for.
Ready to talk about Data Engineering?
Thirty minutes with an engineer, not a salesperson. You will get a straight answer on scope, timeline and cost.