About
I'm Danny Nakhla.
Bridging executive AI strategy with twenty years of production engineering.
I am an AI systems architect and strategist based in Philadelphia and New York. I help enterprise organizations translate ambiguous business goals into resilient, production-ready AI systems with clear governance, cost controls, and operational safety. The opinions here are mine.
How I got here
My career was built on high-concurrency platforms and global software delivery. At the agency Ammirati, I engineered consumer web infrastructure for Sony and Jerry Seinfeld's Comedians in Cars Getting Coffee, maintaining zero downtime through massive traffic spikes. My work since has spanned Nintendo, Yelp, and a Super Bowl campaign across media, retail, and entertainment. At a global publisher, I led international engineering teams and shipped 27 products across 11 countries in under a year, then built automated migration pipelines that reduced content migration timelines from weeks down to hours.
By 2020, I had live machine-learning systems running for real users. Since 2022, I have focused on enterprise AI adoption: advising CTOs and VPs of Engineering on model routing, evaluation loops, LLM cost optimization, and human-in-the-loop workflows across finance, pharma, media, and logistics. Today, I help large organizations get their AI past initial prototypes into daily production use.
What I am building now
The current portfolio covers six live systems. Penny manages day planning, morning briefings, email triage, and background task execution with explicit approvals. LetMeCheckThatBot lives inside group chats to fact-check claims, parse media, and retrieve thread context. Doorbell Intelligence indexes raw camera feeds for semantic video retrieval and two-way audio. BlockReport turns Philadelphia's public property records into grounded answers using a structured data layer and domain-scoped tool routing. KTrader separates prediction-market research, decision evaluation, risk gates, and audit logs. ScooScoo.homes converts real-estate addresses into custom generative art. Build decisions are documented in the Lab.
Products before these
I have been founding and shipping software products of my own since 2009. ClosetBank, a virtual closet the New York Times wrote about, grew to thousands of daily users. FrameIQ analyzed short-form video frame by frame to pinpoint where clips held or lost attention. When a product has run its course, I preserve the build record as a case study.
What "agents and retrieval systems" actually means
The agents are software that takes a goal, decides the steps, and acts on its own. The retrieval systems give a model the right facts to answer from, pulled from a body of documents at the moment of the question. The work I get called in for is usually some combination of the two: an assistant that can both reason and reach for the real source of truth.
If you're building AI that has to work after the demo, I'd like to hear about it.