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Nazlı Şipi

Nazlı Şipi

4 Articles

Nazlı is a data analyst at AIMultiple. She has prior experience in data analysis across various industries, where she worked on transforming complex datasets into actionable insights.

She is also part of the benchmark team, focusing on large language models (LLMs), AI agents, and agentic frameworks.

Nazlı holds a Master’s degree in Business Analytics from the University of Denver.

Latest Articles from Nazlı

Agentic AISep 24

Benchmarking Agentic AI Frameworks in Analytics Workflows

While agentic frameworks share the goal of empowering LLMs with tool usage and reasoning, their architectures reveal critical differences in decision-making, error handling, and data processing. We had previously benchmarked agentic frameworks across different use cases, but we wanted to observe how these frameworks would behave and perform on a more complex task.

Agentic AISep 24

Vision Language Models Compared to Image Recognition

Can advanced Vision Language Models (VLMs) replace traditional image recognition models? To find out, we benchmarked 16 leading models across three paradigms: traditional CNNs (ResNet, EfficientNet), VLMs ( such as GPT-4.1, Gemini 2.5), and Cloud APIs (AWS, Google, Azure).

AISep 24

LLM Latency Benchmark by Use Cases

The effectiveness of large language models (LLMs) is determined not only by their accuracy and capabilities but also by the speed at which they engage with users. We benchmarked the performance of leading language models across various use cases, measuring their responsiveness to user input.

Agentic AISep 24

Top 5 Open-Source Agentic Frameworks

I reviewed several popular open-source AI agent frameworks. In this article, I break down each framework’s multi-agent orchestration capabilities, agent and function definitions, memory handling, and human-in-the-loop support, exploring how each one operates and how easy it is to get started.