Seven service areas, one way of working
Every engagement follows the same five steps — analyse, design, build, monitor, deliver — whether the scope is a single integration or a full reporting platform. These are the areas the work usually falls into.
Data foundations
What it involves: mapping your sources, designing a data model, and building a governed lakehouse in Fabric where operational and financial data lands automatically, is cleaned once, and is defined once.
Typical result: one reliable place to build reports, integrations and agents on — instead of every report solving the same data problems again.
ERP & finance reporting
What it involves: turning Dynamics 365 F&O data into reporting that finance actually signs off on — ledger-reconciled figures, agreed definitions for revenue, margin and open positions, and a faster month-end. Typical territory: invoice and purchase-order flows, inventory and transfer-order reporting, finance dashboards, and security & licensing analysis.
Typical result: reports that match the ledger, with the reconciliation documented rather than assumed.
Microsoft Fabric implementation
What it involves: setting up Fabric properly — workspaces, capacity, lakehouse structure, pipeline patterns, deployment between dev and production, and access through your Entra ID groups.
Typical result: a Fabric environment your own team can work in confidently, with conventions documented instead of tribal.
Power BI models & reporting
What it involves: building or repairing semantic models — measures, relationships, row-level security — and the reports on top of them. Includes consolidating the "many small models, many small answers" situation into one governed model.
Typical result: reports that are fast, consistent with each other, and cheap to extend.
API & system integrations
What it involves: connecting the systems that need to exchange data — ERP, e-commerce, logistics, planning tools — through documented API integrations with retry logic, error handling and logging built in from the start — including OData and REST entity flows, JSON/XML message processing and scheduled SFTP exchanges.
Typical result: integrations that fail loudly and recoverably instead of silently, and that more than one person understands.
Data agents & automation
What it involves: building scoped, read-only agents on top of the governed data layer — agents that map business questions to documented sources, run bounded queries, and hand off to reporting with their assumptions written down. Plus the unglamorous automation around it: scheduled checks, alerts, recurring data tasks. During an engagement this can include interactive drilldown apps on sanitized aggregates — the analysis phase made explorable.
Typical result: the recurring "quick question" traffic gets answered safely without pulling an analyst off real work. See the demo →
Data quality & operational cleanup
What it involves: the work nobody budgets for and everybody needs — duplicate customers and vendors, inconsistent item data, missing reference data, and the recurring checks that keep it from drifting back.
Typical result: master data reliable enough that reports and integrations stop inheriting its problems.
Start with the analysis step
Every engagement begins the same way: an analysis of your existing data sources and reporting. It's a small, bounded piece of work that produces a written assessment — and it tells both of us whether the bigger work is worth doing.
Three ways answers get delivered
Different questions deserve different vehicles. Every format reads from the same governed data layer and the same definitions — the difference is lifespan and audience.
Governed Power BI
Reporting on the shared semantic model — reconciled, secured with row-level access, and cheap to extend. The standing answer to recurring questions.
Agent-built reports
Documented HTML deliverables produced by Albert — findings, definitions and a Data Basis block in every report. Two samples are public.
Interactive drilldown apps
Lightweight apps on sanitized aggregates for the diagnostic phase — filter, slice and drill through a data-quality or reconciliation question before any permanent report is built. Where the question is temporary, this replaces a reporting project entirely.
One engineer plus governed agents, delivering what used to need a team — that's the point of the ladder.
The five steps behind every service
Analyse the existing data sources
Inventory, reliability, ownership, and what the reporting actually needs.
Design the data model
Business concepts defined once, in writing, with named owners.
Build pipelines, notebooks or API integrations
Automated, versioned, re-runnable flows in your environment.
Add logging, error handling and monitoring
Failures alert people; data checks catch drift before users do.
Deliver in Power BI, Fabric or Azure
Working systems plus documentation plus handover — in your tenant.
Scope it small, prove it, then grow it
The best first project is narrow and verifiable — one report, one flow, one integration. If that goes well, the rest follows naturally.