Flexible engagement models that fit how teams actually work

Machine learning initiatives vary widely in scope, maturity, and operational constraints. Our engagement models are structured to support teams at different stages of their ML journey—whether validating an approach, implementing production-ready workflows, or operating models at scale—while aligning to enterprise data, security, and governance expectations.

Our engagement models are designed to meet teams where they are and provide the right level of support at each stage.

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The right level of support.
Tailored to your team.

Whether you’re exploring machine learning in Microsoft Fabric or operating models in production, the right engagement model helps reduce risk, clarify ownership, and keep work moving forward.

Below are the most common ways teams work with Lunar Point Systems, depending on scope, maturity, and delivery goals.

Project-Based Engagements

Project-based engagements are ideal when you have a defined objective and want to deliver a specific outcome within a clear scope and timeline. This model provides structure, predictability, and focused execution, without long-term commitment.

Clearly defined scope and success criteria

Structured delivery timeline with milestones

Production-ready outputs aligned to your environment

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Machine Learning Model Development

Deliver a specific machine learning model from design through production within a defined scope and timeline.

Fabric ML Enablement

Establish Fabric-native machine learning workflows and implementation patterns as a focused engagement.

Architecture & Implementation Guidance

Validate architecture and define a clear implementation path aligned with Microsoft Fabric design standards and enterprise governance before committing to build.

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Advisory & Pairing Engagements

We provide senior-level guidance while your teams remain hands-on. This engagement model accelerates decision-making, reduces delivery risk, and builds lasting capability, without fully outsourcing implementation or ownership.

Structured working sessions aligned to your delivery milestones and Fabric implementation approach

Architecture and implementation guidance aligned to your environment

Timely technical guidance to validate approaches, reduce rework, and maintain forward momentum

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Machine Learning Model Development

Build production-ready machine learning models with ongoing senior oversight to ensure correctness, performance, and operational readiness in production.

Model Monitoring & Lifecycle Management

Establish monitoring, retraining guidance, and lifecycle practices to keep models reliable, explainable, and aligned to business KPIs after deployment.

Fabric ML Enablement & Operational Readiness

Establish Fabric-native machine learning workflows, governance patterns, and operational practices to ensure models can be reliably deployed, monitored, and maintained by your teams.

Build, Deploy, and Operate Machine Learning in Microsoft Fabric

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.

Start a conversation about your Fabric roadmap

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Flexible engagement models that fit how teams actually work

The right level of support.Tailored to your team.

Build, Deploy, and Operate Machine Learning in Microsoft Fabric

The right level of support.
Tailored to your team.