“One of the best product partners I have had. He never loses sight of impact. What stood out most was how close he stays to the customer.”
Carey RansoneDirector, Strategy and Operations at DoorDash · 2026
Product manager. A decade of AI experience.
Starting with computer vision in 2017, now agentic AI at DoorDash.
10 yearsBuilding applied AI products
70+Enterprise deployments
10M+Consumers reached
$MM+Cost savings and revenue
Era 1 of 5 2025 to now
Agentic
DoorDash
Currently scaling and leveling up the voice and chat AI experiences across DoorDash, Wolt and Deliveroo.
Built a 0 to 1 co-pilot for the most complex cases a marketplace produces, in production within six months of project start. The 8% cut in handle time is worth millions a year.
8% lowerHandle time
10% higherQA scores
20kAgents using it daily
Era 2 of 5 2022 to 2025
Generative
Laivly
Invented Sidd Spark (patent pending), a GenAI contact-center suite, pitched and sold alongside the C-suite. Built the operational engine that scaled it from zero to 70+ enterprise brands in 15 months.
70+Enterprise brands
33% lowerHandle time
3mo to 1wkDeployment
Era 3 of 5 2021 to 2022
AutoML
Varicent
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.
ShopifyFirst customer
$1B+Market
200+Interviews
Era 4 of 5 2020 to 2021
Big Data
Fleet Complete
Built a 0→1 AWS ML platform unifying IoT data from 600k+ vehicles; launched the company's first end-to-end ML product, fuel forecasting; and shipped an external model-inference pipeline for real-time predictive maintenance.
600k+Vehicles
Weeks to hoursModel training
Era 5 of 5 2017 to 2020
Computer Vision
Triage
Pioneered an AI dermatology app, full product lifecycle: built the world's largest real-world skin-disease dataset (0 → 500k+ images) and a 50+ dermatologist labeling network, ran clinical benchmarks against physicians and dermatologists, and navigated a dynamic regulatory environment to the FDA's door.
0 to 500kImages
1M+App users
90%+Top 5 accuracy
What colleagues say
12 recommendations · four companies · every year from 2019 to 2026“Quickly established the gold standard for a true Product Management professional. Spearheaded a truly transformative, landmark product, fundamentally changing our business trajectory.”
Jeff SaundersVP Software Engineering at Laivly · 2025
“Relentless about understanding and breaking down customer problems no matter how ambiguous. He knows when to lead from the front and when to lead from the back.”
Faris HijaziManager at Fleet Complete, now Product Lead at Google · 2021
“Working under Mike’s leadership was the period of my steepest career and 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 have ever worked with. His support for product design is particularly noteworthy: he collaborates closely with designers, offering thoughtful feedback.”
Shine ChaudhuriLead Product Designer · 2025
“One of the most effective product leaders I have collaborated with. A rare combination of strategic vision and practical execution. He makes everyone around him more effective.”
David SheridanVP Sales and Country Lead Canada at Laivly · 2025
Read the other six
Jeff JanzenVP Strategic Growth and Partnerships, Laivly · 2025
“A rare combination of confidence and humility. He will 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 and 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.”
Ehsan HaghighatgooData Architect, Fleet Complete · 2021
“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.”
What I have learned building AI products
Four lessons
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. Build the first few by hand, then scale with an LLM. That is how real performance comes.
The lesson that keeps repeating: you cannot simulate or assume an eval set. An eval has to reflect the real conditions the AI will work under. Clinical images from doctors and photos taken on a phone are radically different distributions. And contact center policy says one thing while agents handle cases another way, so your eval has to bridge that gap.
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. 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 are not easy, but they are the game changing piece.
Feedback loops are not magic. Today they take an enormous amount of human review, and I do not diminish that effort: I embrace it, doing the review myself until the value is clear. Then I build the machinery that scales it 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 is AI behind it. It has to actually provide value.
Verified work
Six items
- 2025 AI Excellence AwardAwarded for Sidd Spark, the GenAI product suite I invented and led.Source
- Everest Group PEAK MatrixProduct platform named Major Contender, Conversational AI 2024.Source
- US$6M investment and licenseMyFiziq (ASX:MYQ) invested up to US$6M, US$3M in cash, to license Triage’s AI.Source
- Published researchCo-author, “Q&A-informed image-based deep learning for skin disease recognition in the real world”, SOCML 2018.Paper
- FDA submission, 2018Main author of the FDA submission for an AI software medical device, written with Apple’s former head of regulatory compliance.
- AI versus cliniciansDesigned and ran clinical studies benchmarking the AI against physicians and dermatologists.
Mike Aristocrat is a product manager based in New York City. Over nine years he has built AI products end to end across five periods of AI: computer vision at Triage, big data and ML platforms at Fleet Complete, no-code autoML at Varicent, generative AI at Laivly, and agentic AI at DoorDash, where he builds the agent co-pilot and voice and chat AI across DoorDash, Wolt and Deliveroo. Products he built have shipped inside 70+ enterprise brands and reached more than 10 million consumers.