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Nazlı Şipi

Nazlı Şipi

AI Researcher
25 Articles
Stay up-to-date on B2B Tech
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

DataJun 26

Best AI Web Scraping Tools: Bright Data, Oxylabs & Apify

Sites change their layout and the fields you need from a page shift over time. These changes break manually-coded scrapers. AI scrapers can be updated with simple prompts and are able to self heal to provide consistent results. We benchmarked top AI web scraping tools across the top 10 e-commerce domains to see their performance,…

AIJun 19

Top 9 AI Providers Compared

The AI infrastructure ecosystem is growing rapidly, with providers offering diverse approaches to building, hosting, and accelerating models. While they all aim to power AI applications, each focuses on a different layer of the stack. We benchmarked the most widely used providers on OpenRouter: Cerebras, DeepInfra, Fireworks AI, Groq, Nebius, and SambaNova, using the GPT-OSS-120B…

AIJun 17

LLM Observability Tools: Weights & Biases, Langsmith

LLM applications have expanded from single turn chat into multi step agents that call tools, query databases, and coordinate with other models, which makes their behavior harder to interpret. Each model output results from prompts, tool interactions, retrieval steps, and probabilistic reasoning that cannot be directly inspected. LLM observability addresses this challenge by providing continuous…

AIJun 10

LLM Latency Benchmark by Use Cases in 2026

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 response times to user input. We focused on two key metrics: First Token Latency,…

DataMay 20

Top 6 Food Delivery Scrapers: Benchmark & Use Cases

We benchmarked 6 web scraping providers to see how they handle food delivery data scraping, sending 12,000 requests in total across the top 4 food delivery platforms, and measured success rate, completion time, and metadata coverage. Food delivery data scraping benchmark overall results See the benchmark methodology section for more details on the testing process.…