Our data is messy and spread across spreadsheets — is that a problem?

No. That's the normal starting point. Cleaning and joining the data is often the biggest part of the work, so we plan for it from the first day instead of treating it as a surprise. If something important is missing or too patchy to trust, we tell you early — before you pay for analysis built on top of it.

Do we need a data warehouse or new software first?

No. We start with what you already have — spreadsheets, exports, the database behind your shop or booking system. A data warehouse (one central store for all of a company's data) is worth building only when your data and questions outgrow that. If you reach that point, we'll explain why and what it costs before we build one.

How accurate will a forecast be?

We can't know before we've seen your data, so we won't promise a number up front. What we do is test it: we hold back a stretch of your past data, let the forecast predict it, and compare its guesses with what really happened. You see that gap before you decide whether to rely on it — and if it isn't good enough, we say so.

Is this the same as automating our reports?

No, though the two often meet. If someone rebuilds the same report by hand every week, that's our Automation & Integration work: we make the report build itself. Data analytics starts where the report stops — when you have the numbers but not the answers. Why did sales drop? Which customers are we losing? What will next month look like?

Can my team keep using it without you?

Yes, and we build it that way. Dashboards are designed for the people who will read them, every analysis comes with plain notes on how it was done, and the code and data setup live in your own accounts. Your team — or anyone you hire next — can run it again, change it and build on it. You can keep us on to add to it, but you won't have to.

What do you need from us to start?

The question you want answered, a sample of the data — an export or a few spreadsheets is enough to begin — and someone who knows the business well enough to tell us when a number looks wrong. We only need to read your data, not change it, and we'll sign an NDA first if you want one.

// We Build It

Data Analytics

You have years of data in spreadsheets and systems, but still decide by gut feel. We turn it into clear answers: what is happening, why, and what comes next.

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Overview

Most businesses already collect the data that could answer their biggest questions. It sits in sales records, spreadsheets, booking systems and accounting tools. But nobody has the time to bring it together and work out what it says, so big decisions still come down to gut feel. Data analytics — the everyday work of a data scientist — closes that gap: what is happening in your business, why, and what is likely to happen next.

We start with the decision you need to make, not with the data. Which products to stock more of. Which customers are about to stop buying. Where the money really goes. Then we look at what you have, clean it up — duplicates, gaps, the same customer entered under different names — and find out what it can honestly tell you. Sometimes the answer is "not yet". When that happens we say so, and tell you what to start recording so it can answer next time.

Then we make sure you can trust the numbers. Before you rely on a forecast, we test it on past data it has never seen and show you how close it came. Every result comes with how sure we are, explained in plain words instead of hidden in a chart. A clear "we don't know" is worth more than a confident guess.

What you get is something you keep using, not a one-off slide deck. We bring the data from your different tools into one clean place so it can be analysed together, build dashboards that stay up to date on their own, and write down how it all works. The code and the data stay in your accounts, so your own team can keep asking new questions without waiting for us.

What We Build

Dashboards that show the numbers you care about in one place — sales, stock, customers, cash — kept up to date from your own systems
Cleaning messy data — duplicates removed, gaps found, the same customer under different names merged into one — so every number built on it can be trusted
Analysis that answers a real question — what changed, why, and which products, customers or locations are behind it — written up in plain words
Forecasts trained on your own history — sales, demand, stock — tested on a past period they have never seen before you rely on them, and given as a range, not just one number
Predictions about your customers — who is likely to buy again and who is likely to stop — so you can act before it shows up in your sales
Data pipelines that bring data from your different tools — your shop, your accounting, your spreadsheets — into one clean place, so it can be analysed together
A handover you can use: the code, the data setup, and plain notes on how each analysis was done, so your team can run it again and build on it

Ideal For

You have years of sales and customer records, and big decisions still come down to gut feel.
Sales went up or down last quarter, and nobody can say for sure why.
Your numbers live in different tools that never quite agree, so nobody fully trusts any of them.
You order stock, plan staff or set budgets by guessing, and the wrong guesses cost you money.

Need one of these — or something close to it?

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//How It Starts

01
Tell us what it has to do
A few lines is enough: what it should do, who will use it, and any date you're working to. A senior engineer reads it — the person who would build it.
02
We ask before we quote
A call about the problem, not a spec sheet: who it's for, what the first version must do, and what can wait for the second.
03
One price, or an honest hourly
Once you've decided to go ahead, you get one fixed price and one timeline when the scope is predictable, and hourly billing when it genuinely isn't.

//Selected Work

No published project sits under this service yet. Tell us the question you want your data to answer, and we'll tell you honestly whether it can.

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// How We Work With You

In writing, before you commit.

If you've brought in outside engineers before, you know where it goes wrong: the senior on the sales call isn't the one doing the work, the code lives in someone else's account, the price moves, and your data is shared before anything is signed. So those four go in writing first.

The engineer who scopes it stays on it

You meet the senior engineer who will lead the work — by name, on the first call — and they stay accountable for all of it to the end, including work by any developer we bring in. No handoff to a stranger once the contract is signed.

Your repository, your IP, from day one

You own the code the moment it's written, not when the final invoice clears. It lives in your repository, under your account, documented so any developer can pick it up. No lock-in, and nothing held hostage.

The price is agreed before the work starts

Fixed price, hourly, or a fixed monthly fee — whichever fits the work, you see the number and how it's billed before you commit, and it doesn't change unless you agree to the change first.

NDA before you send us anything

Ask and we'll sign yours, or send ours, before you share a repository, a database, or a business plan. We'd rather do the paperwork first than ask you to trust us with it.

//FAQ

Our data is messy and spread across spreadsheets — is that a problem?: No. That's the normal starting point. Cleaning and joining the data is often the biggest part of the work, so we plan for it from the first day instead of treating it as a surprise. If something important is missing or too patchy to trust, we tell you early — before you pay for analysis built on top of it.
Do we need a data warehouse or new software first?: No. We start with what you already have — spreadsheets, exports, the database behind your shop or booking system. A data warehouse (one central store for all of a company's data) is worth building only when your data and questions outgrow that. If you reach that point, we'll explain why and what it costs before we build one.
How accurate will a forecast be?: We can't know before we've seen your data, so we won't promise a number up front. What we do is test it: we hold back a stretch of your past data, let the forecast predict it, and compare its guesses with what really happened. You see that gap before you decide whether to rely on it — and if it isn't good enough, we say so.
Is this the same as automating our reports?: No, though the two often meet. If someone rebuilds the same report by hand every week, that's our Automation & Integration work: we make the report build itself. Data analytics starts where the report stops — when you have the numbers but not the answers. Why did sales drop? Which customers are we losing? What will next month look like?
Can my team keep using it without you?: Yes, and we build it that way. Dashboards are designed for the people who will read them, every analysis comes with plain notes on how it was done, and the code and data setup live in your own accounts. Your team — or anyone you hire next — can run it again, change it and build on it. You can keep us on to add to it, but you won't have to.
What do you need from us to start?: The question you want answered, a sample of the data — an export or a few spreadsheets is enough to begin — and someone who knows the business well enough to tell us when a number looks wrong. We only need to read your data, not change it, and we'll sign an NDA first if you want one.

Tell us what it has to do.

A few lines is enough. A senior engineer reads every brief — the person who would do the work.

Talk to an engineer →