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Cem Dilmegani

Cem Dilmegani

Principal Analyst
706 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, automation, cybersecurity (including network security, application security), data collection including web data collection and process intelligence.

Cem's hands-on enterprise software experience contributes to his work. Other AIMultiple industry analysts and 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

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

Agentic AISep 1

AI Agents for Competitive Intelligence: Tools and Applications

The competitive intelligence landscape is shifting rapidly. MarketsandMarkets projects the global AI agent market will grow from USD 5.1 billion in 2024 to USD 47.1 billion by 2030, at a CAGR of 44.8%. Traditional methods, quarterly reports, and manual research are being replaced by AI agents that continuously track competitors, delivering insights in real-time.

Agentic AISep 9

The 7 Layers of Agentic AI Stack

The rise of agentic AI has introduced a technology stack that extends well beyond simple calls to foundation-model APIs. Unlike traditional software stacks, where value often concentrates at the application tier, the agentic AI stack distributes value more unevenly. Some layers offer strong opportunities for differentiation and moat building, while others are rapidly becoming commoditized.

Agentic AISep 9

Agentic Mesh: The Future of Scalable AI Collaboration

While much has been written about agent architectures, real-world production-grade implementations remain limited. Building on my earlier post about A2A fundamentals, this piece highlights the agentic AI mesh, a concept introduced in a recent McKinsey.

Agentic AISep 3

MCP Security: Best Practices and Avoid Common Pitfalls

The model context protocol (MCP), pioneered by Anthropic, is quickly becoming the go-to standard for connecting large language models (LLMs) to the outside world.  But the same simplicity that makes MCP so powerful also makes it risky.

Agentic AISep 4

Building a No-Code AI Lead Generation Workflow with n8n

I have been reviewing popular AI sales agents, including  AiSDR and Outreach.io. While these platforms support lead management, they are typically focused on broader sales engagement and delivered as commercial packages with costs ranging from $2K to $5K per user per month.

DataSep 4

Synthetic Users Explained: Top 7 AI User Research Tools

Traditional research requires weeks of finding participants, scheduling interviews, and analyzing results manually. Synthetic user platforms enable teams to create thousands of realistic user profiles instantly, allowing them to test ideas, messaging, and user flows.

Agentic AIOct 1

Top 15 AI Agent Observability Tools: Langfuse, Arize & More

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

AISep 8

GPU Concurrency Benchmark

We benchmarked the latest NVIDIA GPUs, including the NVIDIA (H100, H200, and B200) and AMD (MI300X), for concurrency scaling analysis. Using the vLLM framework with the gpt-oss-20b model, we tested how these GPUs handle concurrent requests, from 1 to 1024.

Agentic AISep 18

AI Apps with MCP Memory Benchmark & Tutorial

We compared various memory tools using LangChain’s ReAct Agent and four different Model Context Protocol memory servers to determine which performs best. Also, we explored how to integrate Claude with Cursor to implement context-aware shared memory with OpenMemory MCP. This integration allowed us to demonstrate how memory is retrieved and managed in real-time.

Agentic AIAug 23

Optimizing Agentic Coding: How I use Claude Code

AI coding tools have become indispensable for many tasks. In our tests, popular AI coding tools like Cursor have been responsible for generating over 70% of the code required for tasks. With AI agents still being relatively new, I observed some useful patterns in my workflow that I want to share.