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Power BI dashboards that people open without being asked. A warehouse and pipelines underneath them. KPI definitions agreed once, and a foundation the AI work can stand on later.

Two reports, two answers, one meeting wasted.

Most reporting arguments are not about the numbers. They are about the definition: whether revenue is booked or invoiced, whether a customer who bought twice is one customer or two, whether last month ended on the last working day. Until that is written down and enforced in one place, every dashboard is somebody’s opinion with a chart on it.

What sits under a dashboard.

The chart is the last thing built. Most of the work is agreeing what the numbers mean and getting them into one place.

  • KPI definitions agreed with the people who argue about them, then written down
  • A warehouse that holds the agreed version, not four system exports
  • Pipelines that refresh on a schedule, with failures that are visible
  • Power BI dashboards built for a question, not for a screenshot
  • Row-level security, so people see their own numbers and not everyone else’s
  • Historic data loaded, so a trend goes back further than this year
  • Data quality checks that catch a duplicate customer before a report does
  • A foundation the AI work can stand on, rather than one it has to fix first

How one source of truth gets built.

  1. We settle the definitions

    Revenue, customer, month end. Written down and signed off before anyone builds a chart on top of them.

  2. We build the middle

    Pipelines into a warehouse that holds the agreed version, refreshed on a schedule and monitored like anything else.

  3. We build the view

    Dashboards for the questions people actually ask, with security so each person sees their own numbers.

Built once, maintained on the plan.

The warehouse, the pipelines and the first dashboards are a project, scoped once the definitions are agreed. After that the platform is part of your estate: monitored, backed up and patched under your CarpaCare plan, with new reports and changes coming out of the project hours the plan includes.

Projects, migrations and build work are quoted separately and covered by the same SLA.

The rest of this group.

The other three in this group. None of them works well until the foundations underneath can carry it, which is the order we build in.

Two people seen from behind at a wall of yellow, blue and teal sticky notes, one of them reaching up to place a note.
AI, automation and data

AI Readiness Assessment

Use-case workshops. Data and process maturity audit. Risk and ROI scoring. A 90-day roadmap.

A desk with a laptop showing a chat assistant panel, a tablet on a stand showing a chart dashboard, and a small yellow rubber duck between them.
AI, automation and data

AI Implementation

Custom Copilots and assistants. Document and email automation. Secure Azure OpenAI. Guardrails and evals.

A desk with a document scanner feeding printed pages, a stack of paper beside it and two monitors showing a process flowchart.
AI, automation and data

Automation and Custom Software

Power Automate and n8n. Internal tools and portals. API integrations. Invoice processing.

Do two of your reports disagree?

They usually do, and it is usually a definition rather than a bug. A short call and a look at both. No audit fee, no obligation.