You’re in good company

A few of the companies we’ve worked with.

When the numbers start the meeting

Most of the information a business needs already exists, spread across the systems it runs on. Ask two teams for last month’s revenue and you may get two answers, each right by its own definition. Reports are rebuilt by hand each month, and a change in one system can quietly break a report nobody knew depended on it.

So the time set aside for a decision goes on working out which figure is right.

An example invitation to a monthly review with Sales, Finance and Operations. The first item on the agenda, agreeing last month’s revenue figure, took the whole hour, and the decision that was meant to follow was moved to next month.
An example business question, which products still make money once returns are counted, and the data its answer needs: orders, returns and product costs, which the business systems hold, and shipping costs, which are only in a spreadsheet.

Start from the decisions, not the data

A data platform is only as useful as the questions it can answer. So we start with the decisions your teams make and the questions behind them, then trace where each answer lives today: which systems hold it, how it moves, who relies on it and where the figures differ.

That shows what to build first, and what can stay as it is.

Your data in one place

Data is collected from the systems it lives in and brought into one store built for reporting and analysis, so heavy reports don’t slow down the systems your teams work in.

One customer, as the store keeps them: when they joined, on the Starter plan at Mill Lane; after changing to the Team plan; after moving to Station Yard, the version a report for the end of last quarter reads; and today, on the Business plan at Station Yard.

  • Pipelines from your systems

    From your business systems, databases, files and APIs, on a schedule.

  • One store for reporting

    A warehouse organised for analysis, so new sources and new questions don’t mean starting again.

  • History kept

    Changes kept over time, so a report can show what was true last quarter as well as today.

  • Access by role

    Each person sees the data their role needs, and personal data is masked where a report doesn’t need it.

Need your systems to update each other as the work happens? That’sSystems & API Integration

One meaning for every figure

Most disagreements about numbers are disagreements about definitions. We settle them once, write them down and check the data against them every time it loads.

An example definition, written down once and used the same way in every report: an active customer is one who placed a paid order in the last year. Its owner is Finance, and it is used in the monthly review, the customer report and the board pack.

  • Shared definitions

    Customers, orders and revenue defined once with the people who use them, and counted the same way in every report.

  • Quality checks

    Each load checked for missing, duplicated and unexpected values before it reaches a report, with an alert to the person who can fix the cause.

  • Data contracts

    An agreement with the team that produces each source about its shape and meaning, so a change upstream doesn’t quietly break a report downstream.

Read: Data contracts: how to stop downstream breakages

Reports people can use

The work pays off when people use it, in the tools they already open every day.

Choose a figure to see where it comes from.

Customer reportUpdated this morning
Where this comes from
Active customers
DefinitionPlaced a paid order in the last year
SourcesShop ordersFinance payments
Checks passedNo duplicatesNo missing daysWithin the usual range
Last updatedThis morning
Where this comes from
Revenue
DefinitionPaid orders, less refunds
SourcesFinance paymentsShop refunds
Checks passedMatches the finance ledgerNo missing days
Last updatedThis morning
Where this comes from
Returns
DefinitionReturned items as a share of items sold
SourcesShop ordersWarehouse returns
Checks passedNo duplicatesEvery return linked to its order
Last updatedLast night

{{ repLive }}

  • Dashboards in your own tool

    Reports built in the BI tool your team already uses, around the decisions they support rather than every figure available.

  • Every figure explained

    Each figure linked to its definition, the sources it came from and when it last updated, so a question about a number has an answer.

  • Self-serve for analysts

    Clean, documented tables your analysts can query directly, without rebuilding the same logic in every spreadsheet.

  • In your own products

    The same figures fed into your software and customer portals, from the same definitions your team uses.

What AI needs from your data

AI features depend on the information behind them. An assistant needs current, approved documents and records it’s allowed to read. A forecast needs history that has been checked. We prepare both: documents kept current and organised for search, records with clear permissions, and the same checks as the rest of your data.

Training, deploying and watching models is the next step, and an easier one when the data underneath is reliable.

AI Chatbots & Assistants AI Agents

Kept reliable as things change

Sources change, new questions arrive and now and then a load fails. We can look after the platform with you: watch each load, fix failures at their source and add new sources as they come. Or we hand it over with documentation, tests and the reasoning behind every definition.

Work with us as a dedicated team, on a fixed price project or a mix of both.

Compare engagement models

Let’s connect it better

Tell us which systems hold your data, which reports matter most and where the figures differ. We’ll talk through the options and suggest a sensible first step.

Thanks, we’ll be in touch soon.

{{ ctaStatus }}