our approach
The focus of pragmatism and relentless AI rigor applied to YOUR PROBLEM Your AI is only as good as your data and its customized for relevance and speed
Step 1: focused pragmatism
We start by understanding your problem through business translation.
Project Scope Definition:
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Problem Definition.
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Data Source Assessment.
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Project Scope Recommendation.
Step 2: structure
Recommended Data
Map/Spine &
Learning Model
Architecture
Step 3: relentless AI rigor
We apply the latest machine learning and best practices in developing the intelligence loop that creates dependable, reliable data outcomes.
Data Transformation:​
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Data modeling & validation
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Visualization
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App development & sharing
We meet you where you are in your AI journey
Eisengard AI is a partner and "accelerator" regardless of where you are on your data and AI journey.
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Eisengard AI's Data Maturity Model
Collection & Architecture
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"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
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"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
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"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
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"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
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"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
Technology
Empower Your Data Decisions with 2 AI Engines
Optimized Data integrations and Model validation is enabled by 2 AI engines that work together in delivering the most reliable and real time insights and predictive recommendations
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AI
Co-Pilot
Generative
AI
Data
Source
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Data Access & Interoperability
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Data Quality, Cleaning & Manipulation
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Deep Learning on Structured Data
Reliable
Live Insights
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Analytics
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Supervised Learning
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Reinforcement Learning
Customized
App
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Data Visualization
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Natural Language Processing
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Generative AI - LLM's