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Nazlı Şipi

Nazlı Şipi

AI Researcher
37 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

Data
Benchmark
Jul 14

Top 5 Amazon Review Scrapers Compared

To compare how web data scraping providers handle Amazon review extraction, we tested 5 web scraping providers on the same set of Amazon product review URLs, totaling 2,500 requests across all providers. Read our benchmark methodology for more detail on our testing process. Amazon was the most accessible platform in our reviews scraping benchmark. The…

Data
Benchmark
Jul 2

Top 4 Google Play Scraping Providers Compared

We benchmarked four web scraping providers across Google Play product page URLs, sending 4,000 requests in total. For each request, we measured how reliably the provider returned data, how long it took from submission to final response, and how many metadata fields the response contained. Only providers with a success rate above 90% were included…

Data
Benchmark
Jul 2

Top 6 Apple App Store Scrapers: Bright Data, SerpAPI & Zyte

We benchmarked 6 web scraping providers against 1,000 Apple App Store pages, for a total of 6,000 requests, and measured success rate, completion time, and the number of metadata fields each provider returned. Since all providers achieved 100% success rates, we focused our comparison on the number of metadata fields returned and end-to-end response times.…

Data
Benchmark
Jul 2

Top 5 Job Posting Scraper APIs Compared

We benchmarked 5 leading web scraping providers across 5 major job platforms by running 12,500 requests in total, then measured each provider’s success rate, completion time, and metadata output. You can read benchmark methodology section for more details on the testing process = supported, returns HTML = supported, returns structured data = no data returned…

AI
Benchmark
Jun 30

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). Mean Average Precision (mAP) served as our primary accuracy metric, supplemented by latency, cost and class-specific…

AI
Benchmark
Jun 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…

Data
Insight
May 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. See the benchmark methodology section for more details on the testing process. Different platforms expose different layers of data,…