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Cem Dilmegani

Cem Dilmegani

Principal Analyst
724 Articles
Stay up-to-date on B2B Tech
Cem has been the principal analyst at AIMultiple for almost a decade.

Cem's work at AIMultiple has been cited by leading global publications including Business Insider, Forbes, Morning Brew, Washington Post, global firms like HPE, NGOs like World Economic Forum and supranational organizations like European Commission. [1], [2], [3], [4], [5]

Professional experience & achievements

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. [6], [7]

Research interests

Cem's work focuses on how enterprises can leverage new technologies in AI, agentic AI, cybersecurity (including network security, application security) and data including web data.

Cem's hands-on enterprise software experience contributes to his work. Other AIMultiple industry analysts and the tech team support Cem in designing, running and evaluating benchmarks.

Education

He graduated as a computer engineer from Bogazici University in 2007. During his engineering degree, he studied machine learning at a time when it was commonly called "data mining" and most neural networks had a few hidden layers.

He holds an MBA degree from Columbia Business School in 2012.

Cem is fluent in English and Turkish. He is at an advanced level in German and beginner level in French.

External publications

Media, conference & other event presentations

Sources

  1. Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
  2. Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
  3. Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
  4. Science, Research and Innovation Performance of the EU, European Commission.
  5. EU’s €200 billion AI investment pushes cash into data centers, but chip market remains a challenge, IT Brew.
  6. Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
  7. We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

Latest Articles from Cem

AIDec 2

AI in Government: Examples & Challenges

AI in government is no longer a hypothetical or early-stage experiment. Public institutions are moving from isolated pilot projects to large-scale and systemic adoption of AI across core government functions: from social services and healthcare to transportation, public safety, and administrative operations.

AIDec 2

Multimodal Embedding Models: Apple vs Meta vs OpenAI

Multimodal embedding models excel at identifying objects but struggle with relationships. Current models struggle to distinguish “phone on a map” from “map on a phone.” We benchmarked 7 leading models across MS-COCO and Winoground to measure this specific limitation. To ensure a fair comparison, we evaluated every model under identical conditions using NVIDIA A40 hardware and bfloat16 precision.

AIDec 2

RAG Frameworks: LangChain vs LangGraph vs LlamaIndex vs Haystack vs DSPy

We benchmarked 5 RAG frameworks: LangChain, LangGraph, LlamaIndex, Haystack, and DSPy, by building the same agentic RAG workflow with standardized components: identical models (GPT-4.1-mini), embeddings (BGE-small), retriever (Qdrant), and tools (Tavily web search). This isolates each framework’s true overhead and token efficiency.

AIDec 2

Top 5 Facial Recognition Challenges & Solutions

Facial recognition is now part of everyday life, from unlocking phones to verifying identities in public spaces. Its reach continues to grow, bringing both convenience and new possibilities. However, this expansion also raises concerns about accuracy, privacy, and fairness that need careful attention.

Enterprise SoftwareDec 2

eCommerce Technologies Use Cases & Examples

The eCommerce sector continues to expand by ~10% each year as more consumers shift their purchasing habits online and seek faster and more convenient digital experiences.This growth is also accompanied by increasing competition, making it essential for businesses to understand how technology is shaping customer expectations.

Enterprise SoftwareDec 2

Database Monitoring Tools: SolarWinds vs New Relic vs Datadog

We installed three database monitoring platforms on a clean system running MySQL to see how they handle database monitoring from scratch. We examined: ease of setup, onboarding experience, agent resource consumption, accuracy in metric measurement, and effectiveness of their alerting systems’ notifications when issues arise under real-world database workloads.

AIDec 2

Top 40+ LLMOps Tools & Compare them to MLOPs

The rapid adoption of large language models has outpaced the operational frameworks needed to manage them efficiently. Enterprises increasingly struggle with high development costs, complex pipelines, and limited visibility into model performance. LLMOps tools aim to address these challenges by providing structured processes for fine-tuning, deployment, monitoring, and governance.

Agentic AIDec 2

15 AI Agent Observability Tools: AgentOps, Langfuse & Arize

Observability tools for AI agents, such as Langfuse and Arize, help gather detailed traces (a record of a program or transaction’s execution) and provide dashboards to track metrics in real time.  Many agent frameworks, like LangChain, use the OpenTelemetry standard to share metadata with observability tools.

Agentic AIDec 1

Best LLMs for Extended Context Windows

We analyzed the context window performance of 22 leading AI models by testing them using a proprietary 32-message conversation that includes complex synthesis tasks requiring information recall from earlier in the conversation. Our findings are interesting. Smaller models often beat their larger counterparts, and most models fail well before their advertised limits.

Enterprise SoftwareDec 1

Compare Top 4 Self-Checkout Systems

Many retailers continue to face challenges at the checkout line, especially during peak hours when long waits, limited staffed checkout lanes, and rising labor constraints converge. These delays affect the overall shopping experience and place additional pressure on store staff who must balance payment assistance, customer questions, and other in-aisle tasks.

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