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Hazal Şimşek

Hazal Şimşek

Industry Analyst
77 Articles
Stay up-to-date on B2B Tech

Hazal is an industry analyst at AIMultiple.

Research interests

Hazal focuses on

  • process intelligence including process mining
  • enterprise automation including IT automation and low-code no-code (LCNC) automation

Professional interests

She has experience as a quantitative market researcher and data analyst in the fintech industry.

Education

Hazal received her master's degree from the University of Carlos III of Madrid and her bachelor's degree from Bilkent University.

Latest Articles from Hazal

DataNov 15

Compare Top 20 Test Data Management Tools

Test data management tools (TDM) ensure quick delivery of high-quality test datasets to development environments, supporting the shift to agile DevOps methodologies.

AINov 17

Benchmark of 11 Best Open Source Embedding Models for RAG

Most embedding benchmarks measure semantic similarity. We measured correctness. We tested 11 open-source models on 490,000 Amazon product reviews, scoring each by whether it retrieved the right product review through exact ASIN matching, not just topically similar documents. Open source embedding models benchmark overview We evaluated retrieval accuracy and speed across 100 manually curated queries.

AINov 11

AI Risk Assessment: 4 AI Risks, Case Studies & Top Tools

As AI becomes central to business operations, AI risk assessment and mitigation is now a strategic priority. Discover four main types of AI risks supported by real-world examples, leading tools, and legal frameworks and policies that help organizations detect, assess, and mitigate AI risks effectively while maintaining compliance and trust.

AIOct 19

LLM Automation: Top 7 Tools & 8 Case Studies 

LLM automation refers to shift to intelligent automation tools that leverage LLMs, including AI agents, fine-tuned LLMs and RAG models to automate and coordinate tasks.  Explore our comprehensive coverage for what LLM automation is, its top real-life applications and major tools.

Enterprise SoftwareNov 24

CPFR: TOP 21 Tools, 6 Case Studies & 5 Benefits

The global market for demand planning solutions, including CPFR (collaborative planning, forecasting, and replenishment) software is growing with the need for real-time data sharing, cloud platforms, and AI-driven forecasting to build more integrated and resilient supply chains.

DataSep 17

Synthetic Data Chatbot: Top 26 Tools to Test and Train Them

Synthetic data is expected to surpass real-world data as the primary source for AI training by 2030, and chatbots are no exception. Once mainly used to train bots when real conversations were scarce or sensitive, it’s now just as vital for testing, validating performance, stress-testing, and ensuring compliance when real logs aren’t safe or available.

Agentic AIOct 27

Top 10+ Agentic Orchestration Frameworks & Tools

79% of executives are already adopting AI agents, although 19% of firms struggle with coordination. They cannot manage agents across different applications. Agentic orchestration offers the solution. Explore agentic orchestration is, its patterns, and the top frameworks that enable multi-agent collaboration.

Agentic AINov 11

Top 10 Agentic AI ERP Systems & 6 Solutions

Gartner predicts that by 2028, one-third of enterprise software will include agentic AI, making up to 15% of daily decisions autonomous. Agentic AI ERP refers to AI agents integrated into Enterprise Resource Planning systems, enabling data analysis, prediction, and autonomous actions.

AISep 1

AI Sandbox Risks & Wins: 30 Tools & 7 Real-Life Examples

Interest in AI sandboxes has surged in recent months. They provide secure environments to develop, test, and deploy AI models without risking sensitive data or system stability.

Enterprise SoftwareSep 11

Top 29 DevOps Automation Tools for Efficient Workflows

35% of last year’s automation efforts were dedicated to DevOps automation, based on a recent report on IT automation. This reflects the growing need for tools that help development and operations teams automate their development process and efficiently implement DevOps practices.