Software-defined Intelligence Research

Innovative Solutions for Connected and Intelligent Technologies

Redefining Intelligence

At 99P Labs, we believe that the future of systems empowering individuals
and society—particularly in areas like mobility—will be driven by the seamless
integration of software, computing, artificial intelligence, and connectivity. Our
research in Software-Defined Intelligence is grounded in this vision, exploring
how these technologies converge to shape the next generation of systems
and solutions.

Software-Defined Embedded Systems: Creating intelligent, hardware-
independent software frameworks that foster seamless cooperation and intuitive interaction within diverse, multi-system environments.

Cloud Continuum and Connectivity: Design and evaluate sustainable,
resilient, and ethical cloud continuum models using open-source frameworks, ensuring scalable and adaptive connectivity.

Informative Data: Create data-driven methods for simulating and analyzing environments to extract valuable insights, leveraging AI to generate actionable intelligence.

Research Highlights

Software-defined Intelligence

Open-Source

PlantD

PlantD is an open-source tool for analyzing and optimizing data pipelines by orchestrating synthetic data generation, measuring cost, latency, and throughput, and managing load patterns. It provides clear metrics and visualizations to help developers quickly assess different pipeline configurations for better efficiency and reliability.

99P Labs Team:
• Rajeev Chhajer
• Brian Coy
• Ben Davis
• Tony Fontana

CMU Team:
• Majd Sakr
• Chris Bogart
• Baljit Singh

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Open-Source

99p labs-f1tenth

99PLabs-F1Tenth is an open-source project built around a 1/10th-scale autonomous race car platform, enabling rapid development and testing of autonomous vehicle software. Through modular Docker-based architecture and simulation tools, it accelerates innovation and safe deployment in both virtual and real-world settings.

99P Labs Team:
• Brian Coy
• Nithin Santhanam
• Ben Davis
• Luka Brkljacic
• Tony Fontana
• Martin Arroyo
• Rajeev Chhajer
• Ryan Lingo

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White Paper

reap

REAP is a method that combines reflection, explicit task decomposition, and advanced prompting to enhance Large Language Models for complex, multi-step tasks. It improves coherence, clarity, cost-efficiency, and scalability by iteratively refining solutions and exploring multiple pathways to better
answers.
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99P Labs Team:
• Ryan Lingo
• Martin Arroyo
• Rajeev Chhajer

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