From disconnected pipelines to answers on demand
An AI-first medallion architecture, with Fabric and Snowflake MCP servers that respect existing permissions.
Fabric and Data Architecture Workshops
Build it with us. Run it without us.
We move analytics teams up the Microsoft Fabric maturity curve. The arc is the same across every workshop: individual analysts, engineers and report developers become a team that builds data systems and runs them without us.
Want us to build and operate it for you instead? Data & Analytics Applications is the other half of what we do.
Where you are
Most teams can place themselves on this curve in about a minute. We work at whichever stage you are in now, and the goal of a workshop is to move you one stage along.
Not sure which one you are on? The readiness assessment is nineteen questions that settle it — free, no sign-up.
Stage 1
Engineered data warehouse / lakehouse
- Medallion architecture — raw → cleaned → business-ready
- AI-enabled development — plugins carry your standards
- Alerting — automated retry and fix processes
- Process quality — data reliability and compute use
- Ready for AI consumption — by end users
Stage 2
BI and FP&A analyst self-service
- Semantic model — standard schemas, minimized sprawl
- AI-assisted dashboard builds — Power BI, Fabric Apps, Claude Artifacts
- Process automation build-outs — Azure Logic Apps
- Git integration and deployment pipelines — source control, promotion path
Stage 3
Business user self-service
- AI tooling with skills — on top of the warehouse
- Awareness and training
- Standard build procedures and definitions — plugin-enabled development
- Self-service publishing within guardrails — business users ship safely
Stage 4
AI-focused workstreams
- Fabric IQ — a business-level map your AI tools can query
- Model selection and planning
- Microsoft Copilot Studio — agents for end users
- Azure AI Foundry toolboxes — curated tool sets for pro-code work
The three workshops
Three workshop shapes, one arc. Scope and timeline depend on how build-ready your data already is — more complex efforts include follow-up sessions. Expect a few hours a week from one or two of your people. Each card turns over for the topics it covers.
Use Case Project
Architect and build, then workshop
- Timeline
- 3–6 weeks of interviews and build, then a two-day workshop
- Who takes part
- Business intelligence and analytics managers, analysts and FP&A, plus data engineers and engineering professionals.
Best when: Your team wants a repeatable process for building new analytic capabilities — robotic process automation, AI workflows, or dashboards and apps built by the people who use them.
Typical topics
- Build Fabric apps using coding agents — citizen developers build their own tools, create template apps
- AI-assisted Power BI and semantic model builds
- Data engineering — building a Fabric medallion around control tables for agentic engineering
- End-user agents — Copilot Studio or Azure AI Foundry — one unified toolbox for agents and security management
- In-house process automation — stand up the architecture and tooling to rapidly build robotic process automation workflows
- Set up Git and Azure DevOps for CI/CD pipelines — deploy safely using AI
Weekly Work Team
Build together, week by week
- Timeline
- 4–6 weeks
- Who takes part
- Business intelligence and analytics managers, analysts and FP&A, plus data engineers and engineering professionals.
Best when: A team needs to come up to speed on practices together, or needs help navigating setting up a new practice area.
Typical topics — worked through with you, not handed over
- Build Fabric apps using coding agents — citizen developers build their own tools, create template apps
- AI-assisted Power BI and semantic model builds
- Data engineering — building a Fabric medallion around control tables for agentic engineering
- End-user agents — Copilot Studio or Azure AI Foundry — one unified toolbox for agents and security management
- In-house process automation — stand up the architecture and tooling to rapidly build robotic process automation workflows
- Set up Git and Azure DevOps for CI/CD pipelines — deploy safely using AI
Process Review
Review your practice, agree a roadmap, keep the standards as plugins
- Timeline
- 2–3 weeks — interviews, then two half-day workshops
- Who takes part
- Business intelligence and analytics managers, analysts and FP&A.
Best when: You are exploring where AI fits, or tightening an existing process for consistency and speed.
Typical topics
- Assess readiness to use AI on your data
- Set up and configure AI tools — Claude, Copilot Studio, Azure AI Foundry
- Benchmark the current process against the capability roadmap
- Agree best-practice standards in the room
- Turn standards into reusable skills and plugins
Every workshop is quoted to scope. Book a call and we will size it against what you already have in place.
Work we have delivered
Newest first, across both service lines. Our current work is four standing relationships with heavy-equipment dealers — most running several months and multiple projects.
Analysts shipping their own apps
Citizen developers now deploy Fabric Apps through GitHub CI/CD, having never used Git.
Automation brought in-house
A weekly team replacing a third-party contractor with Azure Logic Apps, across dev and prod.
Prototypes turned into production
Claude Code work deployed as Fabric Apps with row-level security and Fabric IQ.
Real-time commission dashboards
Migrated a 90-plus rep sales team from monthly commission statements to always-available dashboards with row-level security for reps and managers.
Talk to my data
A natural-language interface over a Snowflake warehouse. Ask in plain English, get answers back without writing SQL.
Accord 25 insurance form analysis
A Streamlit app that checks insurance certificates against legal standards by vendor type — compliance verification for vendor submittals on the spot.
Power BI automation pipelines
Continuous delivery for Power BI reports — automated testing, deployment and version control.
Territory management optimization
Data-driven analysis for a sales geography restructuring — balanced workloads against coverage efficiency.
Start with a call
Twenty minutes to work out which workshop fits, what it would cover, and what it costs.