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Problem Solver

Ken Fischer is founder and CEO of Atigro, a Washington DC-based agency specializing in custom software development, practical AI tools & solutions, ERP transformation, and mobile & web applications for companies and nonprofits nationwide.

He leads our team of engineers and technical managers in the US and Hyderabad, India. Ken’s business acumen combined with his technical expertise ensures that technology is thoughtfully applied to create long-term value for clients. He has implemented over 100 enterprise solutions successfully.

Ken attended Washington University School of Medicine in St. Louis and the University of Maryland Baltimore County.

Credentials

Washington University School of Medicine Masters of Science

University of Maryland Baltimore County Bachelors of Art

what-is-agentic-ai

What Is Agentic AI & Is It Delivering On Its Promises?

Agentic AI was supposed to be the natural evolution of generative AI. A tool that could “make decisions” with holistic and relevant contextual knowledge, plan and articulate independently, learn and adapt over time, pursue goals autonomously, and take action by converting its planning outputs into actionable execution.

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Data streams moving rapidly through space.

ERP Transformation Through AI Augmentation

“ERP transformation through AI augmentation” is a mouthful, but every word is a building block that explains what happens when an enterprise resource planning system is transformed with a network of AI modules rather than refactoring, rearchitecting, or rebuilding it.

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Human hand reaching out to a stream of data.

Information on Demand: Data Democratization in the Age of AI

Data democratization is broadly described as capturing more data and making it more widely available to more people. It’s a straightforward definition; however, it’s odd that we aren’t questioning it more now that the sheer volume, variety, and velocity of data the world creates is almost incomprehensible.

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retrieval-augmented-generation-rag

Understanding Retrieval Augmented Generation

In the rapidly evolving landscape of artificial intelligence, the fusion of generative models with advanced retrieval techniques has paved the way for Retrieval Augmented Generation (RAG). This innovative approach enhances the accuracy and contextual relevance of responses produced by large language models (LLMs).

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