What would they like to hear more of? I am thinking: Building a scalable Generative AI product

#analytics #data-collection #quality-assessment #productivity #AI # #Data-Science
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Abstract

In today's rapidly evolving software engineering landscape, building scalable AI-native software engineering intelligence analytics platforms requires both technical expertise and strategic vision. With AI-generated code becoming increasingly prevalent in enterprise environments, measuring its effectiveness has become critical for organizations seeking to optimize their development investments. Success depends on 3 fundamental foundations.

 First, prioritize engineering data fidelity and contextual intelligence over raw metrics volume. Our AI analytics are only as valuable as the engineering signals they interpret. Implement sophisticated data collection mechanisms that capture traditional metrics alongside specialized tracking for AI-generated code contributions, including origin identification, quality assessments, and downstream impact analysis. This foundation determines our platform's capabilities and limitations.

Second, embrace architectural flexibility for workflow integration and adaptive scaling—design an analytics platform that seamlessly integrates with existing development toolchains while handling unpredictable data patterns. Cloud-native microservices allow us to iterate on analytics components independently while maintaining system resilience. Include dedicated modules for measuring AI code effectiveness, tracking acceptance rates, modification frequencies, and security patterns.

 

Finally, center development around engineering team productivity and developer experience. Implement tight feedback loops with development teams to understand how they consume intelligence insights.

 

The most impactful AI-native platforms strike a balance between cutting-edge AI capabilities and pragmatic engineering principles. We focus on solving today's delivery challenges and architecting tomorrow's autonomous workflows.


  Date and Time

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  • 960 River Road
  • Teaneck, New Jersey
  • United States 07666
  • Building: Becton Hall
  • Room Number: 205
  • Contact Event Hosts

  • Avi Vatsa (201)-692-2498, avatsa@fdu.edu; Mike Newell, mike.newell2@l3harris.com

  • Co-sponsored by Avimanyou Vatsa
  • Starts 25 September 2025 04:00 PM UTC
  • Ends 29 October 2025 05:10 PM UTC
  • No Admission Charge

  Speakers

Naveen Kumar of Naveen Kumar, Founder & CEO of Truxt.ai

Topic:

What would they like to hear more of? I am thinking: Building a scalable Generative AI product

In today's rapidly evolving software engineering landscape, building scalable AI-native software engineering intelligence analytics platforms requires both technical expertise and strategic vision. With AI-generated code becoming increasingly prevalent in enterprise environments, measuring its effectiveness has become critical for organizations seeking to optimize their development investments. Success depends on 3 fundamental foundations.

 First, prioritize engineering data fidelity and contextual intelligence over raw metrics volume. Our AI analytics are only as valuable as the engineering signals they interpret. Implement sophisticated data collection mechanisms that capture traditional metrics alongside specialized tracking for AI-generated code contributions, including origin identification, quality assessments, and downstream impact analysis. This foundation determines our platform's capabilities and limitations.

 Second, embrace architectural flexibility for workflow integration and adaptive scaling—design an analytics platform that seamlessly integrates with existing development toolchains while handling unpredictable data patterns. Cloud-native microservices allow us to iterate on analytics components independently while maintaining system resilience. Include dedicated modules for measuring AI code effectiveness, tracking acceptance rates, modification frequencies, and security patterns.

 Finally, center development around engineering team productivity and developer experience. Implement tight feedback loops with development teams to understand how they consume intelligence insights.

 The most impactful AI-native platforms strike a balance between cutting-edge AI capabilities and pragmatic engineering principles. We focus on solving today's delivery challenges and architecting tomorrow's autonomous workflows.

Biography:

Naveen Kumar is the Founder & CEO of Truxt.ai, an organization that builds AI-Native DevOps Analytics platforms to transform how enterprises develop, deploy, and optimize software systems. With deep expertise in DevOps, cloud-native technologies, and AI-driven automation, Naveen specializes in helping organizations leverage intelligent analytics to accelerate software delivery while maintaining enterprise-grade security and reliability. He holds three patents in software engineering automation, has guided numerous Fortune 100 companies through successful DevOps transformations and AI implementation initiatives, and is recognized for his innovative approach to integrating AI into software development lifecycles. A thought leader in AI-native engineering platforms, Naveen is passionate about autonomous software operations and building scalable AI architectures.

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