Sıla Ermut
Sıla is an industry analyst at AIMultiple focused on email marketing and sales videos.
Research interests
Sıla's research areas include email marketing, eCommerce marketing campaigns and marketing automation.She is also part of AIMultiple's email deliverability benchmark. She is designing and running email deliverability benchmarks while collaborating with the AIMultiple technology team.
Professional experience
Sıla previously worked as a recruiter and worked in project management and consulting firms.Education
She holds:- Bachelor of Arts degree in International Relations from Bilkent University.
- Master of Science degree in Social Psychology from Başkent University.
Her Master's thesis was focused on ethical and psychological concerns about AI. Her thesis examined the relationship between AI exposure, attitudes towards AI, and existential anxieties across different levels of AI usage.
Latest Articles from Sıla
Recommendation Systems: Applications and Examples
We examined the main types of recommendation systems, key concepts, and real-world applications, and benchmarked LightFM, Cornac BPR, and TensorFlow Recommenders using AUC, Precision@10, and Recall@10. These libraries implement machine learning algorithms to process training data and generate personalized recommendations using collaborative or content-based filtering techniques. Additionally, these libraries implement machine learning models to analyze…
Top 6 AI App Builders: Lovable, Base44 & Glide
We tested the top 6 no-code/low-code AI app builders using 1 prompt across 15 dimensions, including setup, browsing, checkout, design, and usability. Read the benchmark methodology and evaluation to see how we tested these tools. Lovable is best described as an AI-powered low- or no-code app builder with code-first output. Users primarily build through natural…
200+ Leading AI Benchmarks
We curated a list with over 200 AI benchmarks for LLMs, GPUs, cloud GPUs, AI agents, tabular AI, and cybersecurity that are not yet saturated. We found that benchmarking activity was fairly low and steady through 2024–2025, then rose at the beginning of 2026. This reflects the rapid growth of AI systems requiring evaluation, especially…
Top 11 AI in ITSM Use Cases & Examples
Leveraging AI for IT service management (ITSM) tools supports organizations in terms of: See the top 11 use cases of AI in ITSM, examples, and benefits of leveraging AI in ITSM. AI-native ITSM refers to a new way of managing internal support and IT operations where artificial intelligence is not an added feature but part…
Compare Multimodal AI Models on Visual Reasoning
We benchmarked 15 leading multimodal AI models on visual reasoning using 200 visual-based questions. The evaluation consisted of two tracks: 100 chart understanding questions testing data visualization interpretation, and 100 visual logic questions assessing pattern recognition and spatial reasoning. Each question was run 5 times to ensure consistent and reliable results. See our benchmark methodology…
Compare Relational Foundation Models
We benchmarked SAP-RPT-1-OSS against gradient boosting (LightGBM, CatBoost) on 17 tabular datasets spanning the semantic-numeral spectrum, small/high-semantic tables, mixed business datasets, and large low-semantic numerical datasets. Our goal is to measure where a relational LLM’s pretrained semantic priors may provide advantages over traditional tree models and where they face challenges under scale or low-semantic structure.…
LLM Market Share: Compare Usage & Adoption
We analyzed LLM market share by combining usage-based data and web visit estimates to show how demand for large language models is distributed across AI labs and AI applications: Read the methodology to see how we measured and calculated these results. The United States dominated web visits across all four months, consistently accounting for 85.5–90.5%.…
No-Code AI: Benefits, Industries & Key Differences
No-code AI tools allow users to build, train, or deploy AI applications without writing code. These platforms typically rely on drag-and-drop interfaces, natural language prompts, guided setup wizards, or visual workflow builders. This approach lowers the barrier to entry and makes AI development accessible to users without a programming background. Recently, no-code AI has expanded…
Compare Large Vision Models: GPT-4o vs YOLOv8n
Large vision models (LVMs) can automate and improve visual tasks such as defect detection, medical diagnosis, and environmental monitoring. We benchmarked three object detection models: YOLOv8n, DETR, and GPT-4o Vision, across 1,000 images each, measuring metrics such as mAP@0.5, inference speed, FLOPs, and parameter count. To ensure a fair comparison, all images were resized to…
AutoSys: Key Features and User Insights
Interest in Broadcom’s AutoSys is declining (Source: Google Trends) and it has a lower average rating on review platforms compared to most other workload automation tools. This may be partially explained by: Dive into AutoSys’s capabilities, user evaluations, and outlook to learn more: AutoSys was originally developed by CA Technologies and acquired by Broadcom in…
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