Isil Berkun

About Isil Berkun

Most mid-size companies know they need AI — but they\'re stuck. The big consultancies want $300K and six months for a \"discovery phase.\" Hiring an ML engineer takes 6 months and $200K before they\'re productive. And that internal prototype your team built? It\'s still sitting in a Jupyter notebook. Meanwhile, your competitors are shipping AI features and your board keeps asking,\"What\'s our AI strategy?\" I fix this. I use the AI Production Method — a 3-stage process (Discover → Build → Deploy) that takes companies from \"we know we need AI\" to a production system running in your actual operations. Not a prototype. Not a deck. A working system your team uses every day. Results from real engagements: → Contract review: 4.5 hours reduced to 27 minutes (90% faster) → Claims processing: 85% faster, 73% auto-approval rate → Compliance review: 80% reduction in manual work → Clinical document review: 75% faster across thousands of papers → Manufacturing quality: 40% scrap rate reduction I\'ve done this across 10 industries — financial services, healthcare, insurance, manufacturing, legal, logistics, and more. Every case study has specific numbers because I don\'t ship prototypes. I ship production systems. Background: PhD in Electrical Engineering. 9 years at Intel as a Staff AI Engineer. 12 LinkedIn Learning courses teaching AI to professionals. NVIDIA Inception member. I work with mid-size companies employees) that don\'t have AI teams but have operations worth automating. If that\'s you → DM me \"AI\" and I\'ll send you a relevant case study for your industry. Or book a call: https://calendly.com/berkunis-digifab

Experience

1. Principal Ai Engineer

   Digifab Ai

   Jun 2024 — Present

   I design, build, and deploy LLM and agentic AI systems for enterprise clients across 10 industries.What I deliver:→ Production-grade RAG systems, agentic workflows, and LLM evaluation frameworks→ End-to-end: from workflow audit to live deployment with team training→ Measurable results on every engagement (40-90% workflow reduction documented)Client results:• Contract review time cut from 4.5 hours to 27 minutes for a legal services company• Claims processing accelerated 85% for an insurance carrier (73% auto-approval rate)• Compliance review effort reduced 80% for a financial services firm• Clinical document review time cut 75% for a healthcare organization• Manufacturing scrap rate reduced 40% through predictive quality AITechnology: LangGraph, LangChain, RAG pipelines, Vertex AI, GCP, ChromaDB, Pinecone, FastAPI, PyTorchNVIDIA Inception Program member | Stanford LEAD Incubator Finalist

Education

• Sabanci University

  MSc.

  2007 — 2009

• Michigan State University

  PhD

  2009 — 2015

• Gebze Technical University

  BS

  2003 — 2007

Skills

• Data Analytics
• Nanomaterials
• Scanning Electron Microscopy
• Self-Confidence
• Nanotechnology
• Jenkins
• Powder X-Ray Diffraction
• Microfabrication
• Photolithography
• Interpersonal Communication
• Predictive Analytics
• Physics
• Github
• Data Science
• Hfss
• Machine Learning
• Version Control
• Sputtering
• Software Updates
• Ansys
• Optical Microscopy
• Simulations
• Latex
• Hall Effect
• Deep Learning
• Electronic Manufacturing
• Workplace Relations
• Mathematica
• Big Data
• Sensors
• Microsoft Visual Studio Code
• Optics
• Device Characterization
• Mems
• Device Physics
• Solid State Physics
• Control Systems Design
• Experimentation
• Characterization
• Device Design