Vladimir Dyachkov, Ph.D
Editor-in-Chief of itinai.com
AI Product Leader
- Digital Transformation Expert
- Ph.D. in Economics
My expertise lies in transforming complex data into actionable insight, leading cross-functional teams, and ensuring every piece of content on Itinai.com meets the highest standards of quality, accuracy, and real-world applicability.
I believe that AI is only as powerful as the human insight guiding it.
At Itinai.com, I lead our editorial strategy to reflect principles:
As Chief Editor at Itinai.com — a platform at the intersection of artificial intelligence, digital healthcare, and global innovation. With over 15 years of experience in AI product development, agile transformation, and digital strategy, I bring a research-backed, user-centric approach to content leadership.
- Trustworthiness: Every article is fact-checked, and openly sourced
- Experience: Backed by 15+ years in AI, healthcare, and fintech across 10+ countries
- Expertise: Ph.D. in Economics with AI focus, hands-on ML product deployment
- Authoritativeness: Published across high-traffic platforms; built products used by millions
📘 Experience
🌍 Global AI Leadership
- Chief Product Officer, Digital Medical Products (2016–Present)
Led seven AI-driven product launches, including a WHO-based diagnostic tool structured around ICD-10. - Chief Transformation Officer, Lykke (2019–2020)
Oversaw international team restructuring, boosting agility and strategic alignment. - Senior Product Owner, goTRG (2018–2019)
Reduced time-to-market by 50% through agile adoption across eight teams, saving $120K/month in DevOps. - Product Leader, Alfa-Bank (2017–2018)
Managed the top-rated Alfa Mobile banking app, integrated Apple/Google/Samsung Pay services. - CPO, Price.ru (2014–2016)
Implemented AI-powered cataloging for over 30M products, doubling lead-based revenue. - Product Manager, RIA Novosti (2011–2014)
Oversaw content in 18 languages, serving 180M+ users monthly.
💡 Expertise & Focus
- AI/ML & Data Science: Computer vision, NLP, and predictive modeling
- Digital Product Strategy: From idea to launch across global markets
- Agile & Scalable Architecture: Cloud-native, API-first ecosystems (AWS, Azure, GCP)
- User-Centric Design: Figma prototyping, UX/UI, and personalization
- Content Trustworthiness: Ensuring medical, financial, and AI content is sourced, cited, and verified
🎓 Education
- Ph.D. in Economics, TSU – Research focus: Informational Influence in Economic Systems
- Master’s Degree, TSU – Financial Management & Information Systems
Your Success is Our Guarantee
✅ 15+ Years of Experience in AI and Digital Products
We’ve worked with businesses of all sizes—from startups to enterprises—delivering real value through intelligent, scalable solutions.
🛠 Proven Methodologies
We use only time-tested approaches that are focused on results, not buzzwords. Every tool, model, or process we implement is grounded in evidence and industry best practices.
📊 Measurable Outcomes
We define clear goals, track performance, and stay accountable for delivering success. If it can’t be measured, it can’t be improved.
🚀 Free AI Jumpstart
Discover where your business can reduce costs and grow—risk-free. Our AI audit reveals hidden opportunities with no upfront commitment.
🔍 Transparency at Every Step
From the first meeting to final delivery, you’ll understand the roadmap, the stages, and how each action contributes to your business goals.
📬 Contacts
Join the community of AI experts with Vladimir
- 🔗 LinkedIn https://www.linkedin.com/in/uxproduct
- 🔗 X: x.com/vlruso
- 📧 info@itinai.com
- 📱 Telegram: @itinai
Editor-in-Chief itinai.com Picks
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Join us at the Travel Trends AI Summit 2024
The Travel Trends AI Summit, taking place on February 21-22, 2024, will explore the profound impact of AI on the travel industry. Leading experts, including representatives from Microsoft and Deloitte, will share insights on leveraging AI…
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Microsoft teams up with Semafor to use AI tools for news
Microsoft partners with Semafor to help journalists utilize AI for news creation. Semafor, founded by ex-BuzzFeed and Bloomberg execs, launches “Signals” with Microsoft’s backing, aiming to deliver diverse and up-to-date perspectives on global news. The use…
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LLMWare.ai Selected for 2024 GitHub Accelerator: Enabling the Next Wave of Innovation in Enterprise RAG with Small Specialized Language Models
LLMWare.ai: Enabling the Next Wave of Innovation in Enterprise RAG with Small Specialized Language Models LLMWare.ai has been selected as one of the 11 outstanding open-source AI projects shaping the future of open source AI and…
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How Many Keys Are Enough to Play the Piano?
The text discusses using Python, MIDI, and Matplotlib to analyze music and help beginners find the right instrument to learn piano. It explores extracting musical notes from MIDI files, visualizing note distribution using Matplotlib, and understanding…
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MegaScale-Infer: ByteDance’s Revolutionary System for Efficient MoE-Based LLM Serving
Introducing MegaScale-Infer: Optimizing Large Language Model Performance Large language models (LLMs) have become essential in various applications, including chatbots, code generation, and search engines. However, as these models grow to billions of parameters, the challenge of…
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Researchers from ETH Zurich and UC Berkeley Introduce MaxInfoRL: A New Reinforcement Learning Framework for Balancing Intrinsic and Extrinsic Exploration
Challenges in Reinforcement Learning Reinforcement Learning (RL) is popular across many fields, but it has some key challenges: Sample Inefficiency: Algorithms like PPO need many attempts to learn basic actions. Off-Policy Limitations: Methods like SAC and…
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The Future of Neural Network Training: Empirical Insights into μ-Transfer for Hyperparameter Scaling
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Researchers from the University of Tubingen Propose SIGNeRF: A Novel AI Approach for Fast and Controllable NeRF Scene Editing and Scene-Integrated Object Generation
The research team at the University of Tübingen introduces SIGNeRF, a revolutionary approach for editing Neural Radiance Fields (NeRF) scenes. Utilizing generative 2D diffusion models, SIGNeRF enables rapid, precise, and consistent 3D scene modifications. Its remarkable…
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Successful AI Use Cases in Predictive Maintenance: Insights and Trends
Leveraging Predictive Maintenance with AI and IoT Leveraging Predictive Maintenance with AI and IoT As businesses increasingly adopt predictive maintenance systems that integrate Artificial Intelligence (AI) and Internet of Things (IoT) sensors, they are discovering significant…
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Corporate Lawyer – Drafting initial contract templates or retrieving precedent clauses from legal archives.
Professional Summary An AI-powered Corporate Lawyer excels in drafting initial contract templates and retrieving precedent clauses from legal archives. This digital team member performs repetitive and time-consuming tasks with remarkable speed, accuracy, and stability, thereby freeing…
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Shanghai AI Lab Presents HuixiangDou: A Domain-Specific Knowledge Assistant Powered by Large Language Models (LLM)
Shanghai AI Laboratory’s HuixiangDou, an AI assistant based on Large Language Models (LLM), addresses the flood of messages in technical group chats. It provides relevant responses without overwhelming the chat, enhancing efficiency. Using an advanced algorithm…
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Do All the Roads Lead to Rome?
The author discusses using Python, network science, and geospatial data to answer the question of whether all roads lead to Rome. They load and visualize the Roman road network data using GeoPandas and Matplotlib. They transform…
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This AI Paper Outlines the Three Development Paradigms of RAG in the Era of LLMs: Naive RAG, Advanced RAG, and Modular RAG
Researchers have developed a groundbreaking approach, Retrieval-Augmented Generation (RAG), which significantly enhances the accuracy and relevance of Large Language Models’ (LLMs) responses. By incorporating up-to-date domain-specific information, RAG reduces response inaccuracies and hallucinations, bolstering user trust.…
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Korvus: An All-in-One Open-Source RAG (Retrieval-Augmented Generation) Pipeline Built for Postgres
The Challenges of RAG Workflows The Retrieval-Augmented Generation (RAG) pipeline involves multiple complex steps, requiring separate queries and tools, which can be time-consuming and error-prone. Korvus: Simplifying RAG Workflows Korvus simplifies the RAG workflow by condensing…
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AI matches doctors in X-ray analysis, University of Warwick Study finds
A University of Warwick study unveils an AI system, X-Raydar, trained on 2.8 million chest X-rays, demonstrating comparable accuracy to doctors in diagnosing 94% of conditions. It highlights potential for efficient diagnosis, particularly in addressing radiologist…
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Top Open Source Large Language Models (LLMs) Available For Commercial Use