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Product lead — from AI hypothesis to shipped MVP.
Delivered the Attuned AI conversational coaching experience end to end, leading a cross-functional team of 15 from AI hypothesis definition through launch.
Optimized spend through model selection, prompt engineering, prompt caching, and AI evaluation — the full MVP ran on under $120 of software and infrastructure.
Led development of the core conversational AI experience, placing among the top 3 startups in Dr. Nancy Li's AI Product Management Incubator Program.
What we heard across interviews and survey responses.
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Parents weren't looking for another parenting resource. They were looking for help translating existing knowledge into action during emotionally charged moments.
She needs to stay regulated — and reach what she already knows — right when a hard moment hits.
Coa meets parents in four moments — each powered by its own AI workflow.
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The more a parent uses Coa, the more it knows their family — no competitor remembers.
Everything in Attuned AI was built around a single behavioural hypothesis.
If the AI delivers warm, age-appropriate, NVC-framed responses with less than 15% age-stage misattribution to parents of children ages 2–12, they are able to recognize their own emotional triggers and respond to their child in the moment rather than react, which improves D7 session return rate — with 3+ sessions in the first 7 days predicting conversion — ultimately increasing free-to-paid conversion within the first week.
Every product is a series of decisions under uncertainty. These were the choices that shaped Attuned AI.
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Every major product choice was evaluated against one question — will this help parents feel understood and supported in the moment they need guidance most?
Developmental appropriateness, turned into a measurable quality metric.
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Trust depended on developmental accuracy. If guidance felt too mature for a toddler or too childish for a pre-teen, parents were less likely to trust the recommendation, return to the product, or convert.
Turning developmental appropriateness into a measurable quality metric transformed AI trust from a subjective concern into a product KPI tied directly to retention, engagement, and conversion.
Happy to walk through the discovery work, the AI evaluation framework, and the trade-offs.