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Projects

Projects with a clear finish line.

Migrations, platform builds and governed metrics for businesses that already run a data platform. Scoped up front, delivered in phases and handed over properly.

What a platform project changesBefore: each source feeds its own reporting tool. sales_FINAL_v3.xlsx feeds Excel dashboards, an old database feeds Power BI, and an on-premises server feeds both Looker and an AI dashboard. After: source APIs, SaaS apps, databases and files all feed one warehouse on Microsoft Fabric, Snowflake or Databricks, modelled, tested and governed, which serves AI dashboards, natural-language questions and analytics dashboards.BEFOREsales_FINAL_v3.xlsxOld databaseOn-prem serverExcel dashboardsPower BILookerAI dashboardAFTERSource APIsSaaS appsDatabasesFilesWarehouseModelled, testedand governedMicrosoft Fabric,Snowflake orDatabricksAIdashboardsNaturallanguageAnalyticsdashboards

What we deliver

Five kinds of project.

Warehouse migration

Move off an ageing warehouse without losing trust in the numbers.

The warehouse has grown for years. Loads are slow, the logic is buried in stored procedures nobody wants to touch, and every change risks breaking a report someone relies on.

Warehouse migration pathBefore: Legacy warehouse: Stored procedures, overnight loads. Then Step 1: Rebuild: Tested before every build. Then Step 2: QA: Old and new run side by side. Then After: New warehouse: Documented, version-controlled.BEFORELegacy warehouseStored procedures,overnight loadsSTEP 1RebuildTested beforeevery buildSTEP 2QAOld and new runside by sideAFTERNew warehouseDocumented,version-controlled
The old warehouse keeps running until the new one gives the same answers.

What we deliver

  • A map of what’s in the warehouse, what’s used and what can go
  • The logic rebuilt as version-controlled models, with automated tests that stop a breaking change before it builds
  • QA of old against new, so the numbers match before anyone switches
  • A planned switch-over, then the old warehouse retired

How it usually runs

  1. 01Assess. Map the tables, jobs and reports, and who depends on each.
  2. 02Rebuild. Move the logic into documented models, with tests that fail a bad change before it reaches anyone.
  3. 03QA. Run old and new side by side and check every key number until they agree.
  4. 04Switch over. Move reports and users across, then retire the old system.

Afterwards

We can keep the new warehouse stable under a monthly managed service, so the gains don’t erode.

Fix-on-fail support

Cloud migration

From servers you maintain to a cloud platform that scales.

On-premises servers and hand-built jobs take time just to keep alive. Scaling means more hardware, and the platform can’t support newer work such as AI.

Cloud migration pathBefore: On-premises: Servers and scheduled jobs. Then Wave 1, Wave 2, Wave 3. Then After: Cloud platform: Scales on demand, no servers to run.BEFOREOn-premisesServers andscheduled jobsWave 1Wave 2Wave 3AFTERCloud platformScales on demand,no servers to run
Workloads move in waves, so each batch is proven before the next one starts.

What we deliver

  • A target design on the cloud platform that best fits your workloads and team
  • Pipelines moved or rebuilt, in planned waves
  • Security, access and cost controls set up from the start
  • Monitoring and written runbooks for the new platform

How it usually runs

  1. 01Assess. Take stock of workloads, dependencies and what each costs to run.
  2. 02Design. Agree the target platform, security model and order of migration.
  3. 03Migrate in waves. Move workloads in batches, each one tested before the next.
  4. 04Switch off. Retire the old servers once nothing depends on them.

Afterwards

We can run the new platform for you, with monitoring and fixes covered under fix-on-fail support.

Fix-on-fail support

BI migration

Fewer reports, one model, the same numbers.

Years of reports in an old BI tool, many of them copies of each other with slightly different logic. The licences cost money and nobody is sure which version is right.

BI migration pathReport, Report v2, Report FINAL. Then Shared: Semantic model: Agreed measures. Then After: Power BI reports: One version of each number.ReportReport v2Report FINALSHAREDSemantic modelAgreed measuresAFTERPower BI reportsOne versionof each number
Many near-duplicate reports become one shared model with reports built on top.

What we deliver

  • An audit of reports and usage: what to keep, merge or retire
  • A shared semantic model in Power BI, with agreed measures
  • Rebuilt reports that match the numbers people know, or explain why they differ
  • Training for the people who own and build reports

How it usually runs

  1. 01Audit. Find out which reports are used, by whom, and where they disagree.
  2. 02Model. Build one semantic model with the measures agreed up front.
  3. 03Rebuild. Recreate the reports that matter on top of the shared model.
  4. 04Switch over. Move users across, then retire the old reports and licences.

Afterwards

A development retainer keeps the model and reports growing with the business, without the sprawl coming back.

Development retainer

Platform builds

A data platform built in layers you can trust.

You’ve outgrown what you have, or it was put together in a hurry and it shows: no clear layers, no tests, and no single place to find the right data.

Medallion architecture: bronze, silver and gold layersERP, CRM, Files and APIs. Then Bronze: Raw: As it lands. Then Silver: Cleaned: Deduplicated, joined. Then Gold: Business-ready: Modelled for use. Then Power BI, Analysts, AI tools.ERPCRMFiles and APIsBRONZERawAs it landsSILVERCleanedDeduplicated,joinedGOLDBusiness-readyModelled for usePower BIAnalystsAI tools
Bronze keeps the raw data as it arrives, silver cleans and joins it, and gold shapes it for the people and tools that use it.

What we deliver

  • Ingestion from your source systems
  • Layered models, from raw data to business-ready tables
  • Tests, documentation and lineage you can follow
  • An access model, and a release process for every change

How it usually runs

  1. 01Design. Agree the architecture, the layers and what each one is for.
  2. 02Foundations. Set up ingestion, environments and the release process.
  3. 03Build out. Add data one subject area at a time, tested as it lands.
  4. 04Hand over. Documentation and training, so your team can work on it.

Afterwards

Once it’s live, fix-on-fail support keeps it stable and a development retainer keeps adding to it.

Managed services

Governed metrics

One definition of each number, used everywhere.

Finance, Commercial, Operations and Product each calculate the same key metric their own way. Reviews turn into arguments about whose figure is right, and nobody wants an AI tool reading numbers the business doesn’t agree on.

Governed metrics: from conflicting definitions to one semantic layerFinance, Commercial, Operations, Product. Then Agreed: One definition: Owned and documented. Then Served from: Semantic layer: Tested, governed. Then Reports, Analysts, AI agents.FinanceCommercialOperationsProductAGREEDOne definitionOwned anddocumentedSERVED FROMSemantic layerTested, governedReportsAnalystsAI agents
Four teams, one agreed definition, served from one place to every report, analyst and AI agent.

What we deliver

  • Agreed definitions for your key metrics, each with a named owner
  • A semantic layer that serves them, tested so a broken definition never reaches a report
  • A clear line between what data engineering owns and what analytics engineering owns
  • Governed access for reports, analysts and AI agents

How it usually runs

  1. 01Find the conflicts. Document how each team calculates the metric today.
  2. 02Agree. Settle one definition with the owners, and write it down.
  3. 03Build. Put it in the semantic layer, with tests.
  4. 04Serve. Point reports, analysts and AI at the one source, then retire the copies.

Afterwards

New questions need new metrics. A development retainer adds them to the semantic layer the same governed way.

Development retainer

How we run projects

No surprises at the end.

  • Scoped before we start

    You see the phases, the deliverables and what done looks like before any work begins.

  • Delivered in phases

    Each phase ends with something you can see and check, not a big reveal at the end.

  • Proven before switch-over

    Old and new run side by side until the numbers agree.

  • Handed over properly

    Documentation and training, so your team can own what we built.

Get a tailored quote

After the project

Keep it running, keep it moving.

A project ends, but the platform doesn’t. The work can move straight into a managed service, so someone keeps looking after it.

Platforms

The platforms we build on.

  • Microsoft Fabric
  • Power BI
  • dbt
  • Snowflake
  • Databricks

Moving to or from something else? Ask us on a discovery call.

FAQ

Common questions.

How long does a project take?

It depends on what’s there today and how far it needs to go. After a discovery call we send a phased plan, and the proposal sets out the timings.

Do you work with our existing team?

Yes. We plan the work around the people you already have, and shape the handover so they can take it on.

What happens when the project ends?

We hand over the work with documentation and training, so your team can run it. If you want ongoing help, it can move into a managed service.

Next step

Book a discovery call

A short call to talk through your platform, your team and what is getting in the way.

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