We start by understanding your problem through business translation.
Project Scope Definition:
​
-
Problem Definition.
​
-
Data Source Assessment.
​
-
Project Scope Recommendation.
Recommended Data
&
Learning Model
Architecture
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
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.
Analysis
​
"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.
Analytics
​
"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.
Proteus
​
"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