Data integration that gives your reports one agreed set of figures
Pipelines move data out of the systems that hold it. A warehouse or lakehouse keeps it in one organised place. Links keep your daily systems in step with each other.
For whomFor IT and data leads, finance teams and operations leads whose reports disagree, or who are preparing data for AI.
- Your ERP
- Your CRM
- Spreadsheets and files
- One data store, with agreed definitions
- Reports and AI on ordered data
Reports and AI fail first where the data was never put in order
A warehouse or lakehouse is a central store organised around the questions you ask. Each figure in it has a written definition, agreed with the person who owns that figure. Pipelines load the store on a schedule, and quality checks run after each load. When a check fails, the data owner gets an alert before anyone reads a wrong report. The platform runs in your cloud region, on your own servers, or on ours.
We list every source before we move any data
List the sources
For each system that holds the data, we record its owner, how it can be read and how far it can be trusted.
Pipelines
Data from the ERP, CRM, databases, files and APIs, loaded on a schedule. We monitor every pipeline, and a failure alerts a person.
Warehouse or lakehouse
Data organised around the questions your organisation asks. The person who owns each figure agrees its definition.
Links between systems
APIs and automatic updates keep your daily systems in step. A change in one reaches the others without a manual export.
Quality and handover
Data quality checks on a schedule, documentation for every data model and pipeline, and a walkthrough for the team that will run it.
The person who owns each figure agrees its definition before anything is built on it.
Example
Two reports that disagree on revenue end up with one definition
A hypothetical case, to show how the work runs.
- Finance reports monthly revenue from the ERP. Sales reports a higher figure from the CRM.
- Listing the sources shows why. The CRM counts an order when it is signed, and the ERP counts it when it is invoiced.
- The finance lead owns the figure and agrees one written definition. Revenue counts when it is invoiced.
- The pipeline loads both systems into the warehouse. A quality check compares the two totals after each load.
- Both reports now read from the warehouse. When the totals drift apart, the check alerts the finance lead.
Every pipeline arrives documented and checked
How we work together
The first area of data is a fixed piece of work. Then we add one area at a time as more systems come in.
- Pipelines and integration code in your code repository
- An organised warehouse or lakehouse, with a written definition for each figure
- Data quality checks that run on a schedule
- Documentation and operating guides for the team that runs it
Questions buyers ask
Do we need a data platform before we use AI?
Not always. An agent or an assistant can often start on one well kept source. A platform is worth it when several systems must agree, or when reports and AI keep hitting the same data problems.
Which systems can you connect?
ERP systems, Microsoft 365 and SharePoint, CRM platforms, SQL databases, shared file folders and internal APIs. Where a system has no API, we look at its database or its exports when we list the sources.
Can the data stay on our own infrastructure?
Yes. The platform can run on your own servers or in the cloud region you choose, and the data does not need to leave either.
Tell us which numbers people argue about
Name the systems you use and the reports that disagree. Our first reply names the area of data we would put in order first, and the figures it would settle.
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