What does Kadmoon do?

We are a Power BI and Microsoft Power Platform consultancy. We take scattered raw data to the dashboards your leadership actually opens, on a governed foundation of Microsoft Fabric, Azure Synapse, and Azure. That covers the data engineering underneath, the semantic model with one definition per KPI, the reports on top, and the Power Platform apps and automations around them. Everything is built in your tenant and owned by you.

Do you only work in the Microsoft ecosystem?

Yes, on purpose. We go deep in Power BI, Microsoft Fabric, Azure, Power Platform, and Dataverse rather than spreading thin across five stacks. That focus is why we can move fast, govern by default, and support what we ship. If your data lives in other systems, we connect to them, but the analytics and BI layer we build and run is Microsoft.

How much does a Power BI project cost?

It depends on scope: how many sources we connect, the state of the data underneath, how many dashboards and KPIs you need, and whether governance and a data platform are part of the work. A focused set of dashboards on clean data is a small fixed-scope project. A full data platform on Fabric with governance is larger. We scope it with acceptance criteria and milestone billing, so the price maps to something you can verify rather than an open estimate.

We already have Power BI but it is a mess. Can you help?

Yes, this is common and it is a lot of our work. We inventory the existing reports, find where KPIs disagree, and rebuild on a governed semantic layer so revenue, margin, and churn mean one thing everywhere. We add row-level security, workspace structure, and Purview where they are missing. You keep working the whole time; we fix the foundation underneath rather than starting over.

How does a project start?

With a one to two week discovery. We start from the decisions you need to make, not the data you happen to have, and map the business questions, the sources, the indicators, and the owner of each number. You leave discovery with a diagnosis and an architecture plan. First working dashboards are typically live by week two, then validated with each area every cycle.

What about tenant-to-tenant migration?

It is a signature practice for us. When you move to a new Microsoft tenant, after a merger, divestiture, or restructure, your Power BI reports, datasets, workspaces, and Power Platform environments do not come along by default. We inventory everything, remap dependencies, and cut over in phases so you do not lose years of BI and automation history or leave users without reports mid-move.

Which regions and company sizes do you serve?

We are based in Austin, Texas and work with clients across the United States. We deliver remotely with in-person sessions when a project calls for it. We work with mid-market companies and enterprise teams; the common thread is that data matters enough to govern it. We are a fit whether you need a first governed dashboard or a full Fabric platform with managed support.

Financial Services

Predictive credit risk and collections

A predictive score and a collections ladder built on Azure ML and Power BI, so the team acts on the accounts most likely to slip.

Azure Machine LearningMicrosoft FabricPower BIDAX

Average 12m PD: 6.8%; probability predicted by the model
90+ delinquency: 4.2%; realized on balance
Active portfolio: $412M; total outstanding balance
Model KS: 0.52; monitored at each retraining

Illustrative figures · fictitious client · under NDA

The challenge

A lender managed a large portfolio with a rear-view mirror, seeing delinquency only after it happened, and could not prioritize collections by real risk.

What we built

We built the data foundation, trained a risk model on Azure Machine Learning, and served the score and a collections ladder through Power BI so the team works the highest-risk accounts first.

A governed portfolio data model by cohort and aging bucket.
A predictive risk score trained on Azure Machine Learning.
A Power BI collections ladder that ranks accounts by risk.

Outcomes

Collections effort aimed at the accounts most likely to slip.
Delinquency seen ahead of time, not after.
Portfolio risk visible by cohort and aging bucket.

The dashboard we delivered

Credit Risk · Predictive score and collections laddermart_credito · Azure ML scoringRefreshed 07/08 04:30Illustrative data

Report pages

Filters

Period

Position Aug/2026▼

Portfolio

All▼

PD band

All bands▼

Hierarchy

Portfolio

Cohort

Contract

Portfolio · consolidated

Average 12m PD-0.7 p.p.

6.8%

probability predicted by the model

90+ delinquency-1.1 p.p.

4.2%

realized on balance

Active portfolio+3.4%

$412M

total outstanding balance

Model KSstable

0.52

monitored at each retraining

Gini

0.61

Contracts scored

84.2K

Recovered this month

$12.4M

PSI (drift)

0.03

Delinquency predicted vs realized

The model anticipates by three months what the old ladder only saw later

RealizedPredicted

6.2%5.6%5.0%4.4%

SepOctNovDecJanFebMarAprMayJunJul

Balance at risk by portfolio

Click to open by cohort

Auto financing concentrates 34% of the balance and the widest PD dispersion across cohorts.

Contracts in the slice

Score per contract, with the action the collections ladder triggers

8 of 160 rows

Contract

Cohort

Balance

PD 12m

Aging

Score

Ladder action

CT-8526642Customer 6717 · masked

2025-Q1

$50K

26.3%

1-30

634

Renegotiation offer

CT-8938165Customer 3553 · masked

2025-Q1

$169K

14.3%

61-90

773

Specialized collection

CT-8876158Customer 2201 · masked

2025-Q1

$158K

9.0%

Current

852

Preventive negotiation

CT-8688645Customer 3127 · masked

2025-Q1

$176K

12.6%

1-30

814

Preventive negotiation

CT-8468165Customer 3106 · masked

2025-Q2

$51K

11.5%

1-30

828

Preventive negotiation

CT-8962829Customer 7078 · masked

2025-Q2

$251K

9.6%

1-30

844

Preventive negotiation

CT-8466739Customer 9282 · masked

2025-Q2

$202K

2.3%

Current

938

Automatic SMS

CT-8805621Customer 2179 · masked

2025-Q2

$100K

27.0%

Current

620

Renegotiation offer

Illustrative dashboard with the structure delivered on the project. Client names and figures are fictitious and under NDA.

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