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Your dashboards tell you what happened. We build the models that tell you what's next.

Lunar Point Systems builds production machine learning inside your Microsoft Fabric environment.

We turn the data you already have into forecasts, risk scores, and decisions your team can act on. Everything runs in your environment, governed and owned by you. No black boxes. No data leaving your walls.

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Delivered inside your Microsoft Fabric environment. Built to be owned and operated by internal teams.

In overpaid claims avoided

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From prototype to production

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You might not have a problem you can name yet. But you can feel the symptoms.

• Your reports tell you what happened last quarter, not what's coming next.
• There's a model somewhere in the business, but it never made it out of a notebook.
• Every decision waits on someone manually pulling and cleaning the numbers.
• You're paying for Fabric or Power BI capacity you're barely using.
• You suspect there's value sitting in your data. You're just not sure what's possible, or where to start.

Most of the teams we work with started right here.
You don't need it figured out before you talk to us.

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You have the platform. You're likely missing the layer that makes it predict.

Microsoft Fabric is where your data already lives, or is heading. It's built to do far more than reporting. The gap for most teams isn't the platform. It's the machine learning layer on top that turns unified data into forecasts, risk scores, and decisions. That's the layer we build.

Am I already paying for this?

Probably. If your team runs Power BI or Fabric, you're already paying for capacity that can train and run models. Most organizations use a fraction of what they own. The predictive work often costs less to start than teams expect, because the platform is already in place.

What can Lunar Point Systems actually do?

Concrete things, mapped to real decisions:
• Forecast demand, so you stop overstocking slow movers and running short on fast ones
• Score risk and flag fraud in real time, instead of chasing false positives
• Catch anomalies in claims or transactions before money goes out the door
• Optimize inventory and routing against cost, not guesswork

Do I need a data scientist on staff?

No. We build the model inside your environment and hand it off with documentation, so your existing team can operate and extend it. You get the capability without hiring for it.

Is my data even ready?

Often it isn't, and that's normal. A model is only as good as the data under it. When the foundation needs work first, we say so and build that, so what comes next is built on something stable. You don't need to have it figured out before talking to us.

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The practical difference for your team.

The usual way
A model gets built in a notebook and never ships
Your data is exported to an outside vendor's platform
You're locked into a black box only the vendor understands
When the engagement ends, the capability
leaves with it
A data scientist hire you can't find or afford
With Lunar Point Systems
It runs in production, governed, from day one
Nothing leaves your environment. We work inside it.
You own the system, the documentation,
and the know-how
Your team owns the ability to operate and extend it
with or without us
The capability, without the headcount
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Built for the real world

We start with your data. We finish with predictions your team runs. Not every team starts in the same place. Some need the foundation built first. Some are ready for a model now. The path is the same either way.

A trustworthy data foundation

Before anything can predict, the data underneath has to be clean, unified, and governed inside Fabric. For many teams this is the first and most valuable step, and everything else builds on it.

Production ML models (where the return compounds)

Forecasting, risk and fraud scoring, anomaly detection, and optimization. Trained and deployed inside your environment, aimed at real decisions and real dollars.

Monitoring & lifecycle systems

Performance tracking, drift detection, alerting, and retraining patterns that keep models reliable as data and business conditions change.

Handoff and ownership

Clear, durable documentation and enablement so internal teams can operate, maintain, and extend models independently. Our qualified team is available for additional monitoring if you need it.

Engagement options

Engagement models designed to align with how teams adopt and operate machine learning in Microsoft Fabric.

Project-Based

A defined engagement focused on delivering a specific machine learning capability or establishing the data and pipeline foundations required to support production ML in Fabric.

Build and Monitor

An ongoing engagement covering model development, performance monitoring, drift detection, and lifecycle management to ensure ML systems remain reliable as data and business conditions change.

Advisory and Pairing

Hands-on collaboration with internal teams to design, build, and operate ML systems within Fabric. Built for accelerating delivery while developing internal capability and ownership.

Your data's ready to do more than report.

We help organizations move machine learning from experimentation to reliable production by designing and implementing Fabric-native ML systems that align with enterprise data, security, and governance standards.

Whether you’re building your first production model or scaling an existing ML footprint, we work inside your Fabric environment to ensure models are owned, operated, and evolved confidently by your teams.

Book a Fabric Review and leave with a clear read on where your environment stands and
the highest-value model to build first. No pitch. You keep the findings.

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Lunar Point Systems designs and deploys production machine learning systems that operate natively within Microsoft Fabric environments.

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