Niklas Hoeppener
Founder • Ariavor
- Five years in controlling
- Closing books and defending the numbers in them
- Then eleven years
- BI consulting across Europe
Decide Faster With Numbers You Can Defend.
We build the reporting layer your finance team runs on, one semantic model feeding both Power BI and Excel, so every entity consolidates the same way and a number means one thing wherever it is read. Your AI has to stand on that same model, which is why we start there.
- Microsoft Fabric
- Power BI
- AI integration
Why AI pilots stall
Your AI inherits whatever your numbers already mean.
AI assistant licences, AI pilots and chatbot wrappers all work as advertised, and that is the problem. When revenue means three different things across your ERP, your CRM and a folder of spreadsheets, the assistant repeats all three with equal confidence, and leadership quietly stops trusting any of the answers. The fix is not more AI, it is the semantic model underneath it.
/01
No semantic layer
Nobody has told the AI how your business fits together. So it guesses, inferring relationships from table and column names, then reports the answer as confidently as a figure someone actually checked.
/02
No governed metrics
Two entities report the same revenue differently, both figures come from a dashboard, and both are correct under definitions that live in people’s heads rather than in a model. Every meeting opens with a reconciliation, and an assistant simply picks one.
/03
No stable foundation
Without a model between your source systems and everything built on them, reports and assistants read raw fields directly. When one field is renamed, a dashboard shows the error, but an assistant keeps answering from incomplete data without saying so.
Not a bet: the direction of the platform
Microsoft just made the semantic layer foundational to AI.
Read the 2026 Fabric and Power BI releases together and they point one way: AI agents grounded in governed semantic models, which Fabric IQ now serves to them as one shared layer. That used to be our contrarian opinion. It is now the published roadmap of the tools you already pay for.
Fabric Data Agents
Ask your data, inside Microsoft 365
Data agents now publish straight into Microsoft 365. But they only answer as well as the governed model they’re grounded in.
Fabric IQ
Governed context for every agent
Fabric IQ turns your semantic models and a business ontology into shared context that AI agents read from, so every agent works from the same definitions. That only helps when those definitions are right.
Agent Skills for Fabric
AI that authors on your model
Open-source skills let Claude and other AI assistants build reports and query semantic models. The results are only production-grade when the model is.
"AI-ready" models
Microsoft's own word, now
Power BI now recommends changes to make models "more AI-ready". That’s precisely the layer we’ve spent a decade building.
Every one of these capabilities assumes a governed semantic model already exists, and Microsoft ships the capability rather than the model. Building that model for you is our work.
Foundation • Activation • Acceleration • Stewardship
Four products. One foundation that carries you from BI to AI.
Each one has a fixed scope and ends somewhere you could stop. Most engagements start at a Foundation Review, and some stay there and get exactly what they need. The ones that go further are the ones whose AI actually works, because they knew where their numbers stood before building on them. Reporting Stewardship is the exception: it is not a step you pass through but a retainer that runs alongside, keeping what works from drifting while your own team owns it.
01/04
Foundation ReviewWhat we checkA clear view of the numbers you already have.
- Findings report
- Reconciliation evidence
- Fix-first list
An expert second opinion on the metrics, the model and the reports you already run. It tells you what works, what does not and what to fix first, before anything is built on top of it.
02/04
Activation SprintWhat we buildReporting your team opens on a Monday.
- Semantic model
- Live report
- KPI briefs
This is where the numbers get defined and built properly: one governed model, KPIs your team has agreed on, and reporting built around the decisions leadership actually makes. The aim is one report a team runs its weekly meeting on, rather than twelve nobody opens.
03/04
Acceleration PilotWhat we testAI on a model it cannot misread.
- Working pilot
- Test set and results
- Guardrail configuration
With the definitions fixed underneath, AI becomes useful instead of risky. One use case at a time, grounded in the model you already trust, tested by your own people and measured rather than demonstrated. “Not yet” is a valid answer here, and a useful one.
04/04
Reporting StewardshipWhat we maintainYour team owns the reporting, and we keep it accurate.
- Monthly health check
- Changes released once a month
- Same working day on a blocker
It is not a step you pass through. It starts once a review or a sprint is done and then runs alongside it, so your own team owns the reports while we make and maintain changes, all within a fixed monthly budget.
Not sure which of the four you need? Take the twelve-question readiness check.
The shape of every project
Data in. Trust out. The middle is the whole job.
Layer 01
Raw sources
Layer 02 • we build this
Governed semantic layer
Layer 03
Trustworthy AI & BI
Selected engagements
Real foundations. Real outcomes.
A sample of client work, anonymised to respect confidentiality. Sectors and outcomes are real; identifying detail is not.
Hospitality & real estate
Foundation then ActivationWhere the group’s numbers came apart
A review of the group’s reporting found every entity closing its books its own way, with consolidation done by hand in spreadsheets. A ten-month Fabric and Databricks rollout followed, and one CFO cockpit now consolidates every entity on shared definitions.
- Microsoft Fabric
- Databricks
- Semantic model
Multi-brand retail
Foundation then Activation then Acceleration then StewardshipSemantic model as the AI on-ramp
We built a governed semantic model across several retail brands, then made it the foundation of an ongoing AI-enablement stewardship. The textbook path from clean data to defensible AI.
- Metric definitions
- AI enablement
- Stewardship
Industrial manufacturing
Foundation then Activation then AccelerationGoverned BI across the plant network
Unified reporting for a manufacturer across its whole production network. Enterprise row-level security means every stakeholder sees exactly their own numbers, and only those.
- Row-level security
- Power BI
- Governance
Omnichannel e-commerce
Foundation then ActivationLive Finance Analytics
Near-real-time financial reporting for an omnichannel retailer, built on Direct Lake and Eventstream. Finance leadership now sees performance as it happens. Not a day later.
- Direct Lake
- Eventstream
- Finance BI
How we work together
You leave the first call with a plan, not a pitch.
No blind retainers. The free consultation ends with a recommended path that is yours either way, and only the audit after it is billed. Each stage is priced before it starts and the figure does not move unless you change the scope, in which case you see the effect on price and dates before you decide. Every stage leaves documentation and guides your team can work from, so you can decide what comes next at any point: with us, with your own team, or not at all.
01 • Free
Roadmap
Part of the free consultation. A recommended path with a price against each stage on it, and the document is yours to keep.
02 • 2 Weeks
Foundation Review
Fixed-price. We read the reporting, reconcile the key figures against their source, and write it all up, including how ready your team is to take it over.
03 • Fixed price
Build
The fix-first list comes first, then the tiers above it, each priced and agreed before it starts, with weekly check-ins. A defect against what we agreed is not a change, so we fix it rather than quote it. We document as we build, with guides and recordings your team can work from.
04 • Your Call
Handover
We train your team, who take over with the documentation and guides already in hand, or we stay on through Reporting Stewardship. Your choice.
Small on purpose
Founder-led. Never a black box.
Ariavor is intentionally small. I lead every engagement myself, from the first call through to handover, so you are never explaining your business twice. The person who scoped the problem is the person who builds it, and the person you call when something breaks a year later. Being small keeps that quick rather than loose: there is no approval chain between your question and our answer, and what we agree is written down as we go, so a project that moves fast still leaves your team the reasoning behind it.
/01
Foundation before features
/02
Defensible, always
/03
Written, not pitched
/04
Decisions, not approvals
/05
Built to be handed over
/06
Remote, close to the work
Published
Our most recent articles
Longer pieces from the build: what Fabric actually does under load, and the modelling decisions that decide whether AI can read your numbers.
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Questions
The things people ask first.
What size companies do you work with?
Mostly SMEs with 20 to 500 people. They’re large enough to have real reporting complexity, yet small enough to need outside expertise rather than a full in-house BI team. Scaling startups outgrowing their spreadsheets are a natural fit too.
Do we have to commit to AI to work with you?
Not at all. Most engagements start at Foundation, and many happily stay there. AI becomes possible once the foundation exists. It’s never a prerequisite.
What does an engagement cost?
The first call costs nothing and carries no obligation. What follows is billed: a fixed-price audit, then build work scoped from that diagnosis, with a written breakdown before anything begins. No open-ended hourly surprises.
The stack we build on
- Microsoft Fabric
- Power BI
- Azure OpenAI
- Databricks
- Snowflake
- Direct Lake
Free consultation
Know Where Your Data Stands In An Hour.
A free 60-minute consultation. No pitch, no invoice. You’ll leave knowing whether your data is ready for AI, and whether we’re a good fit.
Book Your Free Consultation(opens in a new tab)Let’s talk
Drop Us A Line.
Not ready for a call? Send over the shape of the problem, a rough timeline or a question about tooling, and you’ll get a considered answer from the person who would do the work.
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