The data platform, governed and moved off what came before.
Fabric, Synapse, SQL Server, Dataverse and Power BI, with governance, lineage and quality: supported and tuned day to day, migrated from the platforms that came before, and grounded for AI.
Why it matters
We know SQL Server 2012 and SSIS as well as Fabric.
Most data estates are layered: a fifteen-year SQL Server warehouse, SSIS packages nobody opens, WebFOCUS or Cognos reports finance relies on, a lakehouse that never replaced any of it. We keep the batch that feeds the board pack running, and move each piece only when its replacement is proven against the same numbers.
Support, maintain, modernize.
- Fabric, Synapse, SQL Server and Data Factory pipelines against agreed SLAs: capacity, refreshes, incidents and cost
- Overnight batch and SSIS failures; semantic model and Power BI report support
- Data-quality incidents with root cause and a fix
- Fabric capacity tuning, storage rationalisation and query cost reduction
- Purview catalogue, lineage and quality rules; SQL Server patching and high-availability testing
- Master data and entity resolution: one customer, one product, one supplier across systems
- Legacy SQL Server and on-premises warehouses to Fabric or Synapse; WebFOCUS and Cognos to Power BI; SSIS to Azure Data Factory; Oracle to PostgreSQL
- Fixed-fee assessment first, then reusable migration blueprints and time-boxed data mart builds
- Governed semantic models on Dataverse and Fabric, one definition per metric
Depth signals
Every generation of the Microsoft data stack, and the platforms beside it.
- SQL Server 2012 onwards
- SSIS, SSAS and SSRS
- Azure SQL
- Azure Synapse
- Azure Data Factory
- Microsoft Fabric and OneLake
- Power BI
- Purview
- Oracle and PostgreSQL
- Dynamics 365 and Dataverse
- ERP and line-of-business sources
- SharePoint and Microsoft 365
- Azure integration services
- Entra ID
- WebFOCUS, Cognos and third-party reporting
- Data lakes and OneLake
- APIs and flat-file feeds
Data engineers, SQL DBAs and Power BI modellers covering the legacy stack and Fabric, onshore, nearshore and offshore.
Our bench: AtlasiloSQL Server 2014 to Fabric, then a double-digit capacity savingIllustrative A fifteen-year warehouse with 400-plus SSIS packages migrated in stages, each reconciled to existing reports before cutover, then capacity and query cost tuned against the consumption baseline you already see.
Typical engagements.
- Annuity Data platform managed services Pipelines, warehouses, semantic models and reports under SLAs, with capacity and cost management from the same team.
- Fixed-fee Data-foundation and migration assessment Platforms, models, quality, lineage and access read against what an AI use case needs, plus a disposition per object: migrate, rebuild or retire, with effort attached.
- Outcome-based Platform exit or grounded retrieval A reporting-platform exit priced against the retirement date and run-cost target, or retrieval for one use case priced against an answer-quality target.
AI on top
Grounding is a data decision, not a model decision.
- Retrieval designed per use case: chunking, embedding and an evaluation set agreed up front, so drift is caught later
- Row, column and object security inherited from Entra ID through Fabric and Dataverse, so an agent sees exactly what its user sees
- One definition per metric, so Copilot, a Power BI report and a finance agent return the same figure
- A grounded, measured answer inside one quarter before any platform-wide programme, with APEX tracing each answer to its data
Let's connect
Bring us the problem AI keeps almost solving.
A 30-minute call with the people who'd do the work. We'll pressure-test the opportunity, show you what good looks like, and tell you plainly whether it's worth building.