Microsoft Solutions
Cloud Infra, Data & AI Platform
Build a foundation for reliable decisions and AI
Make data usable, build applications and agents that survive contact with production, and modernise the workloads underneath them on a connected foundation that can be operated and governed.
Microsoft platform
Microsoft Fabric and Power BI for the data chain, Microsoft Foundry and Azure AI Search for the AI chain, delivered as one estate rather than separate products.
Enterprise ready
Cloud and on-premises paths are both supported, because not every workload is allowed to move.
Through to production
The work finishes when the model and the dashboard carry a decision, not when the demo runs.
Governance built in
Measure definitions and row level access are set in the semantic layer, and grounding scope, content safety, and human handoff are set in the workload, not per report and not per prompt.
Capabilities that move the work
The disciplines that connect this practice to operational change.
- Data
Data Analytics
The whole chain: ingestion and ETL, warehouse and lakehouse, semantic model, then the dashboard.
Microsoft Fabric - AICopilot Studio
AI Apps
Applications and agents with retrieval, evaluation, and a human handoff designed in.
Microsoft Foundry - Azure Infra
Infrastructure & Apps Modernization
Landing zone, migration, and the application work that makes the move worth making.
Azure Migrate
The problems this practice addresses
What is usually already happening before an engagement starts, and the capability that answers it.
AI that never reaches the operation
You have run several AI proofs of concept, each demonstrated well, but none is serving real users inside a live business process.
- AI proofs of concept
- Integration friction
- Investment without return
The cost of leaving it
AI investment with no return in production.
Decisions that always wait for a person
Every request stops on someone's desk for review, and the queue grows whenever that person is away or pulled into higher-value work.
- Requests arrive
- One approval desk
- Expert time consumed
The cost of leaving it
Expert time consumed by routine approvals.
Data you hold but cannot yet use
Your data sits across many systems, yet two reports on the same question return different numbers and nobody can say which one the business should trust.
- Separate data sources
- Figures disagree
- Decisions wait
The cost of leaving it
Decisions slowed by figures that disagree.
Security you cannot prove
An auditor or customer asks for evidence of your controls, and your team spends days assembling screenshots from separate tools.
- Controls already in place
- Evidence scattered
- Audits drag on
The cost of leaving it
Effort spent proving controls that already work.
Infrastructure that has aged out
Core systems run on hardware past its support window, and every small change is scheduled for a weekend because nobody wants to disturb operations.
- Core systems run
- Foundation out of support
- Change stalls
The cost of leaving it
Maintenance rises while business change slows.
From fragmented work to measurable progress
The order the work runs in, from the first assessment to the evidence that something moved.
Stage 1
Assess
We assess the sources, their quality, and the decisions or tasks currently waiting on an answer.
Stage 2
Foundation
Data lands in a lakehouse or warehouse on a designed model, and the documents an assistant answers from are extracted and indexed with their entitlement intact.
Stage 3
Model
One source of truth: measures defined centrally with row level security, and retrieval designed so an answer cites what it came from and reaches only who may read it.
Stage 4
Prove
The first case runs until its result is measurable against a fixed evaluation set, before the scope widens.
Stage 5
Operate
Pipelines, indexes, cost, and answer quality are watched as a running service rather than a finished project.
Scoped engagements to move from intent to delivery
Engagements for Data Analytics
- Cloud Infra, Data & AI Platform
Data Platform Foundation
A foundation engagement that carries scattered data through ingestion and ETL into one governed data warehouse for reporting and analytics.
- Work modules
- 3
- How scope is agreed
- Scope depends on the number of source systems and reporting requirements, agreed with the practice team before work begins
- Cloud Infra, Data & AI Platform
Data Protection Baseline
Discovery and classification first, then a labelling and loss prevention baseline that reflects what the organisation actually holds.
- Work modules
- 3
- How scope is agreed
- Scope depends on the size of the data estate and the number of regulatory obligations in scope, agreed with the practice team before work begins
Engagements for AI Apps
- Cloud Infra, Data & AI PlatformStart here
AI Readiness Assessment
An assessment that turns scattered AI experiments into a plan for a governed production platform.
- Work modules
- 3
- How scope is agreed
- Scope depends on the number of use cases and systems under review, agreed with the practice team before work begins
- Cloud Infra, Data & AI Platform
Agent Pilot Programme
A pilot engagement that puts one agentic workflow into a real process on Azure OpenAI Service.
- Work modules
- 3
- How scope is agreed
- Scope depends on the complexity of the chosen process, agreed with the practice team before work begins
Engagements for Infrastructure & Apps Modernization
- Cloud Infra, Data & AI Platform
Azure Landing Zone Accelerator
A landing zone engagement that gives a migration its networking, identity, and resilience foundation before workloads move.
- Work modules
- 2
- How scope is agreed
- Scope depends on the number of workloads and the target landing zone design, agreed with the practice team before work begins
- Cloud Infra, Data & AI Platform
Platform Engineering Enablement
Build the pipeline, the environment definitions, and the operating model that let a team ship changes without a release ritual.
- Work modules
- 3
- How scope is agreed
- Scope depends on the number of applications and environments brought onto the platform, agreed with the practice team before work begins