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our approach

The focus of pragmatism and power of rigor applied to your problem.

Step 1: focus pragmatism

We start by understanding your problem through business translation.

Project Scope Definition:

  • Problem Definition.

  • Data Source Assessment.

  • Project Scope Recommendation.

Step 2: structure

Recommended Data


Learning Model


Step 3: rigor

We apply the latest machine learning and best practices in developing the intelligence loop that creates dependable, reliable data outcomes.

Data Transformation:

  • Data modeling & validation

  • Visualization

  • App development & sharing

We meet you where you are in your AI journey

Your private AI is only as good as your data. 

Our data maturity model will promote your accelerated AI transformation.

Eisengard AI's Data Maturity Model

Collection & Architecture

"Quality, Reliability and Interoperability "

Phase 0

In this initial phase, establish a solid foundation for your AI journey. Understand your data, ensuring its ethical use, integrity, and accessibility.


"Descriptive Reports and Accesibility"

Phase 1

Focus on defining problems clearly and exploring your data to diagnose issues. Formulate and validate models while making data accessible through visualization.


"Prescriptive Recommendations"

Phase 2

Leverage historical data for predictive analytics, and design A/B tests to refine hypotheses. Continuously iterate and adapt your approaches.

Optimized Performance

"Real-time & Personalized"

Phase 3

Provide tailored insights, continuously improve your processes, and reduce communication friction to enhance efficiency. Use systems to deliver insights effectively.


"Networked Intelligence"

Phase 4

Enable networked decision-making, facilitating bidirectional insights and fostering a culture of learning and adaptation for fluid and agile operations.

Data Integration + AI learning

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