MICROSOFT PRACTICE  ·  Data and AI

From a use case to
production.

Data platform assessment and modernisation, data engineering and integration, lakes, warehouses and analytics, Microsoft Fabric and Power BI, Azure AI and Azure OpenAI — including the governance that lets any of it go live.

01 THE PROBLEM

What usually brings people here.

Reporting that arrives too late to matter

Numbers assembled by hand each month from three systems. By the time the report lands, the decision it was meant to inform has already been made.

A warehouse that stopped being fed

Pipelines built by someone who has left, failing quietly. Dashboards still render, the data behind them is stale, and nobody is certain which figures are current.

AI stuck at the demo

A convincing proof of concept that cannot go to production — because the data isn't governed, the access model doesn't exist, and nobody owns what the model says.

02 WHAT WE DELIVER

Scope of the engagement.

Platform assessment and modernisation

  • Data estate discovery and platform assessment
  • Target architecture — lakehouse, warehouse or hybrid
  • Migration from legacy SQL and on-premise platforms
  • Microsoft Fabric readiness and adoption planning

Engineering and integration

  • Pipeline development and orchestration
  • Source system integration and change data capture
  • Data quality, testing and observability
  • Semantic modelling for self-service reporting

Analytics and reporting

  • Data lakes, warehouses and lakehouse implementation
  • Microsoft Fabric workspace design
  • Power BI reporting, datasets and row-level security
  • Self-service enablement and report rationalisation

AI and governance

  • Azure AI and machine learning workloads
  • Azure OpenAI and generative AI solutions
  • Retrieval pipelines over governed internal data
  • Data governance, security, lineage and compliance
03 HOW WE ENGAGE

From first conversation to steady state.

01

Assess

Current-state discovery and documentation. Findings before design.

02

Design

Target-state architecture, signed off by your team and ours.

03

Deliver

Phased implementation with tested cutovers and inherited runbooks.

04

Optimise

Review once live — cost, performance, policy and licence fit.

05

Operate

Managed service, or a clean documented handover. Decided upfront.

04 OUTCOMES

What changes when this is done properly.

Described as outcomes rather than numbers. We publish measured figures only where a customer has approved the reference and the measurement method can be stated.

01

One place the numbers come from. A defined platform with owned pipelines, so a figure quoted in a meeting can be traced to a source rather than defended from memory.

02

Reporting people trust enough to use. Refresh schedules, quality checks and lineage that make it clear what the data is and when it last moved.

03

A governed path for AI. Access control, data classification and evaluation in place before a model reaches production — which is usually what the proof of concept was missing.

04

Proof of concepts that can graduate. Built on the production platform from the start, so a successful pilot becomes a deployment decision rather than a rebuild.

05 CREDENTIALS

Certifications relevant to this work.

Microsoft Certified: Azure AI Apps and Agents Developer Associate×4Microsoft Certified: Azure AI Cloud Developer Associate×2Microsoft Certified: Azure Databricks Data Engineer AssociateMicrosoft Certified: Azure Cosmos DB Developer SpecialtyMicrosoft Certified: Machine Learning Operations Engineer Associate

Certifications held across the Trinova engineering team. A multiplier indicates the certification is held by more than one team member. Individual credentials are verifiable through Microsoft Learn on request.

Trinova Cloud is enrolled in the Microsoft AI Cloud Partner Program. We hold no Solutions Partner designation at this time and make no designation claim anywhere on this site.

Ready when you are.

Every engagement starts with a scoped, documented assessment.