From computer vision in 2017 to agentic AI at DoorDash today. I've built AI products more times than I can count, and I have the scar tissue to prove it.
The same playbook, proven across five eras and businesses: data flywheels, evals, humans in the loop, and relentless shipping.
For companies
AI initiatives that move business metrics, not demos.
I come in, learn your business deeply, and find the highest-leverage, practical AI plays, then help you ship them. I've pitched, demoed, and sold to C-suites, and built products CFOs and CROs used weekly. That's how a GenAI concept became a suite inside 70+ enterprise brands in 15 months.
Product strategy, evals design, data annotation ops, pipeline architecture, jumping on customer calls, writing the FDA submission: I've done every job adjacent to the model. The zero-to-one phase, where nothing is defined yet, is where I do my best work.
"Just deploy AI" isn't a strategy. These are the unglamorous things that actually make AI products work. I've built each of them multiple times.
Spend time in the data. Build evals.
Live in the data. Touch the outputs. Read the transcripts until your brain is numb: LLMs will tell you about 33% of what you need to know, and the rest you can only infer yourself. Then build evals. They're your friend, and they're worth it almost every time. Build the first few by hand, then scale with an LLM. That's how real performance comes.
Data strategy changes throughout the product life cycle.
Data strategy is its own competency: getting the data, cleaning it, producing it, organizing it, structuring it. Data moves through phases over a product's life, and what you can build at any moment depends on what your data can support. Early on, that usually means bootstrapping it yourself until the product can improve on its own.
Feedback loops aren't easy, but they're the game-changing piece.
Feedback loops aren't magic. Today they take an enormous amount of human review, and I don't diminish that effort: I embrace it, doing the review myself until the value is clear. Then I build the machinery that scales it (dermatologist networks, Mechanical Turk, classifier-assisted review, automated QA) so ML integrity stays high and the improvement loop is durable.
Production is a different ball game.
Latency budgets, cost-per-interaction economics, hallucination controls, enterprise-grade SLAs. And the one that matters most: no one cares that there's AI behind it. It's gotta actually provide value.
Five eras of AI, many products
From single features to platforms and multi-product suites: every era shipped to real users, with the data flywheels and evals that kept them working.
2025 – nowAgentic AI at scale era
DoorDash · Product Manager, CX & Integrity
AI for a global marketplace, across chat and voice. Built a 0→1 co-pilot augmenting support agents on the most complex cases, lifting efficiency and quality 8%: a multimillion-dollar saving that also improved retention, and more impact in six months than that part of the org had seen in the entire prior year. Now scaling Voice AI to consumers, and bringing AI zero-to-one across DoorDash's global brands: Wolt and Deliveroo.
+8% efficiency & quality20k agents use it daily$MM+ saved annuallyRead the case study →
2022 – 2025B2B Generative AI era
Laivly · Group Product Manager, Applied GenAI
Invented Sidd Spark (patent pending), a GenAI contact-center suite: concept → 70+ enterprise brands in 15 months, pitched and sold alongside the C-suite. Built the evals (NER, intent, transcription WER) that made models swappable, and the scale engine that cut deployment from 3 months to under a week.
Defined and launched Symon.AI, a no-code data & autoML platform that let analysts build ML without engineers, removing the technical barrier to AI in a $1B+ market, pre-generative-AI. Personally landed Shopify as the first customer, building executive dashboards used at the very top of the org.
Pioneered an AI dermatology app: full product lifecycle, from the world's largest real-world skin-disease dataset (0 → 500k+ images) and a 50+ dermatologist labeling network powered by a consensus algorithm I built, to clinical studies benchmarking the AI against general physicians and dermatologists, to the FDA's door.
From 12 LinkedIn recommendations: managers, peers, and direct reports across four companies, every year from 2019 to 2026.
2025
"Quickly established the gold standard for a true Product Management professional… spearheaded a truly transformative, landmark product… fundamentally changing our business trajectory."
Jeff SaundersVP of Software Engineering, Laivly
2026
"One of the best product partners I've had… he never loses sight of impact. What stood out most was how close he stays to the customer."
Carey RansoneDirector, Strategy & Operations · worked together at DoorDash
2021
"Relentless about understanding and breaking down customer problems no matter how ambiguous… exemplifies failing fast and learning quickly. His ego has no place in that."
Faris HijaziHis manager at Fleet Complete · now Product Lead at Google
2025
"Working under Mike's leadership was the period of my steepest career & personal growth… he launched zero-to-one products and drove the organization toward a product-led model."
Victoria PaskannayaProduct Manager · reported to Mike
2025
"One of the most effective Product Managers I've ever worked with… his support for product design is particularly noteworthy: he collaborates closely with designers, offering thoughtful feedback and fostering an environment where creativity thrives."
Shine ChaudhuriLead Product Designer
2025
"He doesn't just hand off new features and disappear, he actively supports our sales efforts by jumping on customer calls… instrumental in driving our revenue growth."
David SheridanVP of Sales / Country Lead Canada, Laivly
Read all 12 recommendations
Jeff JanzenVP of Strategic Growth & Partnerships, Laivly · 2025
"A rare combination of confidence and humility… he'll argue his points firmly and with evidence, but change his mind the moment he learns something that warrants it. His ability to rapidly gather evidence, design, build, test and iterate helped us launch our most successful product yet."
Devashish KhairnarData Scientist & AI Engineer, Laivly · 2025
"A driving force behind our GenAI and LLM initiatives… his leadership in orchestrating our most extensive GenAI data annotation efforts enabled the creation of high-quality datasets, which became the backbone of our deployed AI solutions."
"Under his leadership, the data platform team became an ownership-driven team able to deliver a cloud-based platform enabling adaptability and efficiency for the organization… an excellent product and engineering leader."
Steven NormoreCTO, Triage · 2019
"A true generalist and growth-minded individual, with a relentless desire to understand, reflect, and learn… his combination of strong business acumen with data literacy and customer focus makes him an invaluable asset for any team building products in data and technology."
Robert MichalakReported to Mike at Triage · 2019
"A leader inspiring excellence from those around him… able to coordinate projects amongst different departments and nimbly adjust to ever-changing situations."
Shuang Ao, PhDML Scientist, Triage · 2019
"A true project owner that ruthlessly prioritizes end-to-end processes… he clearly articulates what needs to get done, why it needs to get done, and trusts his team to get those things done."
Changed the FDA's mindMain author of the 2018 FDA pre-submission, written with Apple's former head of regulatory compliance, arguing consumer AI skin screening down from high-risk to low-risk classification.
AI vs. cliniciansDesigned and ran clinical studies benchmarking the AI against general physicians and dermatologists, outperforming average clinician sensitivity and specificity, while simultaneously building the tech.
Clinical pilots + researchStanford, UCLA, and Memorial Sloan Kettering piloted Triage Clinical. Co-author, "Q&A-informed image-based deep learning for skin disease recognition in the real world" (SOCML 2018).
Let's talk.
Scoping your company's highest-leverage AI initiative, building something ambitious, or working on the frontier? I'm in New York, and I'd love to meet you.
