Data Innovation that
Understands Reality
Data Raven was founded to transform data into insights and improve processes across all types of organizations – industrial, public, and commercial. The company is led by Isaac Israel, who brings over 25 years of experience in multidisciplinary technology projects in the fields of AI, ML, and Industry 4.0, including leading global initiatives with companies such as KLA, Amdocs, Orbotech, and SparkBeyond.
We collaborate with local and international companies, delivering innovative, flexible, and practical solutions. Our approach includes full customer partnership – from scoping to development, implementation, and fostering a data culture with advanced technologies.
We collaborate with local and international companies, delivering innovative, flexible, and practical solutions. Our approach includes full customer partnership – from scoping to development, implementation, and fostering a data culture with advanced technologies.
We collaborate with local and international companies, delivering innovative, flexible, and practical solutions. Our approach includes full customer partnership – from scoping to development, implementation, and fostering a data culture with advanced technologies.
Our Unique Approach
Our Unique Approach
AI integration into live production environments
AI integration into live production environments
AI integration into live production environments
AI integration into live production environments
Synergy between technology, operations, IT, and business
Synergy between technology, operations, IT, and business
Synergy between technology, operations, IT, and business
Synergy between technology, operations, IT, and business
Gradual solutions with flexible architecture and secure data practices
Gradual solutions with flexible architecture and secure data practices
Gradual solutions with flexible architecture and secure data practices
Gradual solutions with flexible architecture and secure data practices
Model explainability and interpretability
Model explainability and interpretability
Model explainability and interpretability
Model explainability and interpretability
Building an organizational data ontology (glossary and relationships
Building an organizational data ontology (glossary and relationships
Building an organizational data ontology (glossary and relationships
Building an organizational data ontology (glossary and relationships
Recent articles
Building the Future of Manufacturing
Trustworthy LLMs and Agentic AI
Building the Future of Manufacturing
Trustworthy LLMs and Agentic AI
Building the Future of Manufacturing
Trustworthy LLMs and Agentic AI
Building the Future of Manufacturing
Trustworthy LLMs and Agentic AI
Why Industrial AI Often Misses the Mark — and How to Fix It
What PCB quality control teaches us about making AI truly industrial
Why Industrial AI Often Misses the Mark — and How to Fix It
What PCB quality control teaches us about making AI truly industrial
Why Industrial AI Often Misses the Mark — and How to Fix It
What PCB quality control teaches us about making AI truly industrial
Why Industrial AI Often Misses the Mark — and How to Fix It
What PCB quality control teaches us about making AI truly industrial
Why 95% of Generative AI Pilots Fail ?
And How Viewing AI as “Normal Technology” Can Help You Beat the Odds
Why 95% of Generative AI Pilots Fail ?
And How Viewing AI as “Normal Technology” Can Help You Beat the Odds
Why 95% of Generative AI Pilots Fail ?
And How Viewing AI as “Normal Technology” Can Help You Beat the Odds
Why 95% of Generative AI Pilots Fail ?
And How Viewing AI as “Normal Technology” Can Help You Beat the Odds
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