I build production AI for industries where getting it right actually matters: document intelligence, autonomous agents, and whatever a hard problem turns out to need. Mostly I'm interested in turning messy, manual work into something dependable, and in the people I get to build it with.
When an order-volume surge buried the ops team, IntentIQ became the fix: a two-stage AI pipeline that reads 14K+ emails a day, pulls the latest message and attachments out of tangled threads, triages what is actionable, and routes it into seven title-insurance workflow categories with automated acknowledgements. Urgent items now reach the right desk in minutes, at 95%+ routing accuracy.
14K+ emails / dayMulti-agent · Agents SDK
Healthcare AI
PoppyNote
Clinical documentation that listens. PoppyNote pulls the day's schedule from Curve, records the visit, and drafts the note the way the practice already charts, matching each visit type's format and flagging anything unstated for the doctor to confirm instead of inventing values. One review later, the note files back to the right chart. Built so my wife stops staying late.
1,362 visits managedCase study
Healthcare AI
Ask Poppy
A team of specialist agents answers parents' questions for a pediatric dental practice: appointments, insurance, procedures, emergencies. Every answer is grounded in the practice's own pages before it sends, and emergencies skip straight to urgent guidance. The first line of defense on on-call weekends.
Built out of frustration with uploading private files to random compressor sites. FFmpeg.wasm runs in the browser, so video compression happens entirely in the tab and your files never leave it; images get fast WebP conversion. 50 to 90% smaller, with batch support.
A calm iOS kick counter for the third trimester: one tap per movement, a timed session with pause and undo, calendar history, and CSV export for the next appointment. Everything stays on the phone. No account, no analytics; the App Store privacy label simply reads Data Not Collected.
Tap the orb and say it: two ounces of formula, wet diaper, left side twelve minutes. Chloe logs feeds, diapers, nursing and pumping hands-free with on-device Apple Intelligence, talks back to confirm, and turns the day into caregiver handoffs and pediatrician summaries. An iPhone beta ahead of a privacy-only App Store release, named for the person it was built around.
Dictation that never phones home. Hold Fn and speak: local Whisper transcribes on-device, deterministic rules and an optional local LM Studio model polish the text, and one clean copy lands in the focused app. Cloud AI endpoints are refused in code, so audio never leaves the machine: privacy as an architectural fact, not a policy. A subscription is a tool you rent; this one lives with me.
A decade building systems where the rules are strict.
Doma Technology
San Francisco, CA · 2020 - Present
Senior Manager, Applied AIMar 2023 - Present
Led the applied-AI pod delivering production agentic systems for title-insurance automation, partnering with Fannie Mae and Blend.
Architected DeedIQ, now an issued US patent, and patent-pending ClerkIQ, cutting processing time and per-document cost by about 99%.
Built CI/CD for LLM deployment with canary releases, rollback, and RAGAS quality monitoring.
Helped break a 30-year industry monopoly, catalyzing Doma's shift to a pure-tech platform.
Manager, Data EngineeringJul 2021 - Mar 2023
Built and led a team of six, establishing engineering rituals and modern data practices that scaled company-wide.
Hired and mentored 6 engineers with structured assessments and career frameworks.
Unified three merged companies' data into a centralized Snowflake platform.
10x dashboard performance; 90% reduction in query times.
Staff Data AnalystJan 2020 - Jul 2021
Established a unified source of truth and drove company-wide adoption of modern analytics ahead of IPO.
Federal Reserve Bank of San Francisco
San Francisco, CA · 2013 - 2020
Senior Data AnalystAug 2013 - Jan 2020
Led data engineering and BI infrastructure processing terabytes of economic data for monetary-policy decisions in a highly regulated central-banking environment.
Architected FedQuery Hub, HDFS plus Tableau, enabling self-service analytics for policy research.
Built a Fed-wide data-governance framework that raised reliability across departments.
Patents
A few ideas worth filing for.
Each one started as a stubborn, manual workflow and turned into something that mostly runs itself. The filings are nice; the part I like is that they quietly save people a lot of time.
Patent issuedNo. 19/080655
DeedIQIntelligent deed analysis
LLM agents plus neural search with dual-team extraction, cross-validation, and human-in-the-loop review, replacing a brittle, manual deed workflow with a system that learns continuously.
Hybrid automation across fragmented county portals, with domain-specific parsing, template-driven scripted browsing, AI fallback, and vision verification that every document matches before delivery.
Fully autonomous retrieval for county clerk portals: it discovers the path and turns successful runs into reusable skills, while agentic reasoning corrects bad input data and adapts to portal changes with no human in the loop.
Discovery runs
with Memory MCP to learn a new county portal
100%
autonomous county expansion
Claude Agent SDKPlaywright MCPAuto Skill Generation
Working with people
I'd rather build the team than run it.
A decade in, the part I care about most isn't the systems. It's the people I build them with. I've hired engineers from zero, sat through the boring reviews, and learned that patience ships more than cleverness. No heroics, just steady, honest work.
Built cross-functional pods delivering enterprise AI at scale
Established design reviews, code reviews, and sprint rituals
Structured hiring, mentorship and career-development frameworks
Led teams through IPO prep with governance and security compliance
TA for AI Makerspace Engineering Bootcamp, Cohort 6
Blends RAG, multi-agent systems and fine-tuning with strategy
0 to 60 to 6
engineers hired and grown
14+14+
production AI systems shipped
1,000+1,000+
data-quality issues caught in code review
10+10+
years in regulated AI/ML
Open source & local tools
Small tools, shipped when they helped.
CanopyIQ
Local agent harness
Python-first coding-agent Kanban that turns a large goal into scoped stories, runs one worker at a time, and keeps an auditable review trail, with gates that stop a worker from editing the test it is graded by.
A calmer Gmail inbox powered by Jev that runs entirely on your computer. Evaluates priority, categories, and reply signals with human-in-the-loop label previews, verification receipts, and undo.
Production read and write MCP server for OpenAI's AgentBuilder, with dual Graph plus org-account auth. Powers IntentIQ.
FastMCPOutlookOAuth
Talmanac
iOS beta
Bedtime stories told in a parent's voice. The live site is a beta front door, while the iOS app moves toward a privacy-only local version for saved voices and stories.
Snap a snack photo and Dr. Hawley, a wisecracking molar, roasts it in a kid-safe comic strip. Deterministic dental-risk scoring stays in charge; the AI only does the jokes and the art.
A retrieval-augmented agent grounded in my notes, projects and patents. Ask how a system is put together, how I think about a hard problem, or just say hi. It answers with citations.
Education & credentials
Four degrees, four disciplines.
MS, Data ScienceRegis University
Denver, CO
MBA, Sustainable BusinessSan Francisco State University
San Francisco, CA
MS, Industrial EngineeringUniversity of Central Florida
Orlando, FL
BEng, Chemical EngineeringNewcastle University
Newcastle upon Tyne, UK
Certifications
AI Engineering Bootcamp
AI Makerspace · Teaching Assistant, Cohort 6, mentoring engineers in production LLM, RAG and multi-agent systems.
Advanced LLM Application Building and Fine-Tuning
Maven
Voiceflow Expert Contributor
Featured developer-meetup speaker, KnowledgeFlow and GuardGroq
About
From competitive sport to production AI.
I grew up in Istanbul, played competitive sports, and came up through engineering before finding my way into data and AI. Sport taught me what I still lead by: you rarely win by being exceptional at one thing. You win by being above average at many, and combining them well.
Music makes the same point. There was a time when a musician could write one great song and tour the world on it. That era is gone. The artists creating moments now are DJs: they collect the best tracks and mix them into cohesive sets, each one built for the room in front of them. Building with AI is exactly that. It is not music production, chasing one perfect track. It is DJing: curating the best pieces and blending them into something that moves people.
Home is San Francisco, with my wife Andrea, our daughter Chloe, and our golden retriever Hawley. When I'm not shipping, you'll usually find us out on a long walk, or at home playing records for the family.
I love comparing notes with people building thoughtful AI, a hard problem, a half-formed idea, or just a good conversation. If you're making something interesting, I'd like to hear about it.