Services
Four connected services, one goal.
Know what to fix first, trust the foundation, understand what it's telling you, then let AI take on the recurring work — in that order, because skipping ahead is where most data and AI projects go wrong.
01 — Strategy
Know where to start
The problem
Reporting, data, and AI all feel like they need fixing at once, and it's not clear what to tackle first or what can wait.
What this typically produces
A short, prioritized roadmap that says what to fix first, what to defer, and what to leave alone — not a hundred-page audit nobody reads.
What “good” looks like: You know exactly what to do next, and why it's that instead of something else.
Our approach
- Assess the current data, reporting, and AI landscape
- Prioritize by business impact and effort, not by what's technically interesting
- Map dependencies between the foundation, reporting, and AI work
- Produce a short roadmap, not a shelf audit
02 — Foundation
Trust your data
The problem
Reports disagree, nobody remembers why a metric changed, and every new question means another spreadsheet pulled together by hand.
What this typically produces
A smaller, more reliable set of pipelines and definitions that your reporting can actually be built on — reducing repetitive spreadsheet assembly and conflicting numbers.
What “good” looks like: Two people asking for the same metric get the same answer, without a side conversation to reconcile it first.
Our approach
- Connect information from different business systems
- Automate data preparation and movement
- Establish consistent business definitions and metrics
- Improve data models, validation, and reporting reliability
03 — Reporting
Understand your business
The problem
You have dashboards, but decisions still stall because nobody's sure which number is right, or the report answers “what happened” without ever getting to “so what.”
What this typically produces
A small number of trusted, well-modeled reports your team actually opens — not fifteen versions of the same chart.
What “good” looks like: The report tells you what deserves attention this week, not just what happened last week.
Our approach
- Develop reporting around business questions and decisions
- Build useful Power BI reports and semantic models
- Analyze marketing, sales, revenue, and operational performance
- Investigate changes, conversion bottlenecks, and performance drivers
04 — Applied AI
Put intelligence to work
The problem
AI gets bolted onto a reporting stack that nobody trusts yet, producing confident-sounding answers nobody can verify.
What this typically produces
AI applied to a specific, recurring task — evaluated against whether it measurably improves that task before it's proposed.
What “good” looks like: If AI doesn't clearly improve the task, we say so instead of adding it for appearance.
Our approach
- Help authorized users ask questions about approved business data
- Generate grounded performance summaries
- Assist with recurring analysis and reporting
- Automate suitable workflow steps with appropriate controls
Built on the Microsoft ecosystem
Power BIMicrosoft FabricAzure Data FactorySQL
Supporting technologies we work in, not the reason to hire us — the default is improving what you already run on.
Workflow automation connects all four.
None of these are sold as separate products. A trustworthy pipeline feeds a reliable report; a reliable report is what makes an AI-generated summary worth reading. Where automation makes sense, it's built to connect the work already underway — not sold as a fourth, standalone service.
See case studiesNot sure which of these applies to you?
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