Platform expertise
Data and AI expertise on the Microsoft platform
We design data architecture, build analytics on SQL Server and Microsoft Fabric, and deliver AI solutions with governance and responsible-AI practices.
- SQL Server
- Azure SQL
- Microsoft Fabric
- Power BI
- Azure OpenAI
- Purview
Why this platform
Business problems Data & AI helps solve
Trust the numbers
A governed data platform provides one version of the truth.
Report faster
Fabric and Power BI streamline ingestion, modeling, and reporting.
Use AI responsibly
Guardrails, evaluation, and data controls reduce risk.
Modernize databases
Planned upgrades and migrations improve performance and supportability.
Our expertise
What we do with Data & AI
The areas where our team applies hands-on experience.
Data architecture
Target platform design and standards.
- Lakehouse and warehouse patterns
- Data modeling
- Roadmaps
SQL Server and databases
Reliable, tuned relational databases.
- SQL Server and Azure SQL
- Performance tuning
- Upgrades and migration
Microsoft Fabric
Unified analytics from ingestion to reporting.
- OneLake and lakehouses
- Data pipelines
- Power BI integration
Analytics
Dashboards and self-service reporting.
- KPI definitions
- Semantic models
- Role-based reports
AI use cases
Practical applications of AI and Copilot.
- Knowledge assistants
- Document processing
- Copilot adoption
Data governance
Ownership, quality, and lineage.
- Microsoft Purview
- Data quality rules
- Access policies
Responsible AI
Using AI with fairness, transparency, and accountability.
- Evaluation and testing
- Human oversight
- Privacy and safety controls
Putting it to work
From Data & AI expertise to business results
Services that use it
- Microsoft Fabric & Power BIFabric data platforms, pipelines, and Power BI reporting your teams can trust.
- Copilot Studio & Microsoft FoundryAgents and generative AI solutions, taken from pilot to production with governance.
- Cloud Solutions & MigrationPlan, migrate, and optimize cloud environments that are secure, governed, and cost-aware.
Integrations
Connected to the systems around it
- Azure Data Factory
- Power BI
- Dynamics 365
- Line-of-business databases
- Azure OpenAI Service
- Third-party data sources and APIs
Architecture & governance
Data governance and responsible AI principles
- 1
Know your data
Catalog sources, owners, and sensitivity before building.
- 2
Quality at the source
Validate data as it enters the platform.
- 3
Human in the loop
Keep people accountable for important AI-assisted decisions.
- 4
Evaluate continuously
Test AI quality and safety before and after release.
Adoption path
Grow your platform maturity step by step
- Stage 1
Assess
Understand sources, quality, and priority questions.
- Stage 2
Build
Deliver the platform and first dashboards or AI pilots.
- Stage 3
Mature
Extend governance, self-service, and production AI.
FAQs
Data & AI questions, answered
Something specific in mind? Talk to one of our specialists.
Do we have to use Microsoft Fabric?
No. We recommend the platform that suits your environment, and we work with Fabric, Azure, and others.
How do you approach responsible AI?
Through evaluation, privacy controls, human oversight, and clear accountability for each solution.
Can you modernize old SQL Server databases?
Yes. We assess and plan upgrades or moves to Azure SQL.
Is our data used to train public models?
We design solutions on services and settings that keep your data within your tenant boundaries, and confirm this for each solution.
Talk to our Data & AI specialists
Book a consultation to discuss your goals.
- 1Tell us your goalsShare a few details.
- 2We confirm a timeWe reply with a time that works.
- 3We talk it throughA focused conversation on next steps.