Positioning
I work with companies as a scientific advisor, R&D project advisor, and technical founder in areas where academic research must be transformed into robust, usable software products. My advisory work focuses on artificial intelligence, machine learning, natural language processing, large language models, retrieval-augmented generation, Graph-RAG, Turkish NLP, legal AI, healthcare AI, text mining, and enterprise knowledge systems.
This page is intended for technology companies, R&D teams, product leaders, and university-industry collaboration units looking for an academic or scientific advisor who can contribute to both research depth and product-oriented execution.
Advisory and Industry Experience
Founder & Managing Partner, VeriUs Technology
Founded and lead a university spin-off developing domain-focused AI products and custom AI systems for legal, healthcare, and enterprise knowledge domains. Work includes technical strategy, R&D execution, product architecture, customer-specific integration, and applied AI productization.
Scientific Advisor for Technology Companies
Advised companies on AI, data science, NLP, text mining, deep learning, recommendation systems, enterprise search, and R&D project development. Advisory experience includes work with technology companies and large-scale industrial R&D initiatives.
Research Project Panels and Evaluation
Served as committee member, advisory board member, panelist, and referee for national and international R&D funding programs, including TÜBİTAK ARDEB, TÜBİTAK TEYDEB, Eureka, and Eurostars contexts.
Where I Can Contribute
AI and LLM/RAG Strategy
- Technical roadmaps for LLM, RAG, Graph-RAG, and agentic AI products.
- Architecture review for retrieval pipelines, evaluation pipelines, data ingestion, and source-grounded generation.
- Model selection, fine-tuning, domain adaptation, hallucination mitigation, and benchmark design.
- On-premise, privacy-sensitive, and enterprise-grade AI deployment planning.
Domain-Focused Applied AI
- Legal AI: legislation retrieval, case-law analysis, contract analysis, legal document understanding, and source-grounded legal question answering.
- Healthcare AI: medical NLP, clinical information extraction, radiology report analysis, clinical decision-support interfaces, and medical Graph-RAG.
- Enterprise document intelligence: semantic search, summarization, classification, private document analysis, and knowledge-network construction.
- Turkish NLP and low-resource language adaptation for real-world systems.
Industrial R&D Project Themes
Legal AI and Source-Grounded Legal Systems
R&D on Turkish legislation and legal document intelligence, including retrieval, semantic search, legal question answering, contract analysis, case analysis, and RAG-based legal assistants.
Healthcare AI, Medical NLP and Graph-RAG
R&D on clinical and medical text analysis, medical information extraction, radiology reports, cardiological intelligence, trustworthy medical retrieval, and explainable source-grounded medical AI.
Enterprise Knowledge Systems and Private Document Intelligence
Enterprise information retrieval, private document intelligence, semantic search, summarization, knowledge-network construction, concept extraction, relation extraction, labeling, and hybrid retrieval over private corpora.
Data Science, Recommendation and Text Mining
Industrial projects involving data mining, customer and enterprise analytics, recommendation engines, lead prediction/ranking, request filtering, text classification, semantic quality assessment, and applied machine learning pipelines.
R&D Funding, Proposal Design and Scientific Positioning
Advisory support for R&D proposals, technical novelty framing, work package design, feasibility analysis, evaluation methodology, proof-of-concept planning, and research-to-product translation.
Typical Engagement Formats
Scientific Advisory
Periodic advisory for company R&D teams, technical decision makers, and product groups working on AI, NLP, LLM, RAG, and data science products.
R&D Project Design
Support for project scope, technical novelty, architecture, research methodology, milestones, deliverables, risk analysis, and evaluation strategy.
Prototype and Evaluation Review
Independent review of prototypes, retrieval quality, model behavior, benchmark results, data quality, reproducibility, and product-readiness.
Knowledge Transfer
Short technical workshops, architecture reviews, team mentoring, and applied training for R&D engineers and data science teams.
Publication and Research Output
Guidance for transforming high-quality R&D results into technical reports, academic papers, preprints, datasets, and benchmark studies where appropriate.
University-Industry Collaboration
Connecting academic research depth with practical product engineering, customer requirements, domain expertise, and funding-program expectations.
VeriUs Technology and Product-Oriented AI
Through VeriUs Technology, I work on applied AI products that turn research-grade NLP and LLM techniques into practical systems. Current product directions include legal AI, healthcare AI, private document intelligence, semantic search, domain-specific conversational AI, and custom AI systems integrated with organizational data and workflows.
Contact for Advisory and R&D Collaboration
Companies interested in AI, NLP, LLM/RAG, legal AI, healthcare AI, enterprise document intelligence, or R&D project advisory can contact me directly.
E-mail: murat.ganiz@marmara.edu.tr
LinkedIn: linkedin.com/in/mganiz
VeriUs: verius.com.tr