I led product design for an AI coaching platform — from spec to shipped feature — designing not just the screens, but how the AI coach itself thinks, speaks, and remembers.
Sincera Studio is an AI-powered personal growth coach — an always-available mentor that holds your goals, remembers your patterns over time, and meets you the way you actually need to be met. I led the product design end-to-end, working directly with a developer on implementation, and used AI as an active design partner across the whole lifecycle — from spec to prototype to shipped feature.
People who want to grow — in their career, creatively, or personally — often can't access what actually works: a mentor who knows their history, remembers what they've tried, and is available when they actually need them. That's a privilege reserved for a lucky few.
Sincera set out to close that gap with a personal mentor that's always available — not a generic chatbot, and not a one-size-fits-all productivity app. That created three real design problems:
A coach that doesn't adapt to how you want to be coached won't get used past the first session.
Growth is a pattern across many conversations, not one. The product had to hold that history usefully, not just log it.
Because the product is partly a conversation, much of the design work wasn't screens — it was defining how the AI itself should think, speak, and respond.
I led product design end-to-end, working directly with a developer who owned implementation. My scope covered the full product definition and experience: writing the product spec, designing the core flows (Reveal → Align → Move), iterating on UI copy, and defining how the AI coach itself should behave. AI was a working tool throughout — not just for research, but as an active design and QA partner.
I wrote the product spec conversationally — working from the core insight (people need an always-available mentor) through what it requires as a product: onboarding, goal structure, memory, coaching tone. Stress-testing edge cases in dialogue ("what if a user wants encouragement one day and bluntness the next?") surfaced the coaching-personality problem before a single screen existed.
Before committing to Figma, I prototyped the logic of each stage — Reveal (self-assessment), Align (goal-setting), Move (roadmaps, check-ins) — in conversation. That let me test the shape of a flow (what it asks, in what order, what it does with answers) before investing in high-fidelity screens. Figma was for finishing well-reasoned flows, not discovering problems mid-build.
For a product where the core interaction is a conversation, copy isn't decoration — it's the interface. I drafted and iterated copy across dozens of small moments (onboarding prompts, check-in questions, coaching responses) until the tone matched the product's intent: calm, honest, non-generic.
The most novel part: designing how the AI coach behaves — its tone options, how it references past conversations, where the line sits between "supportive" and "generic." Designing a personality, not just a layout — a discipline that barely existed a few years ago.
Once flows and copy were solid, the developer built each feature; I reviewed every implementation against the spec and flows and fed corrections back. This loop repeated continuously rather than in one big handoff — issues surfaced and got fixed in days, not sprints.
Warm & encouraging, direct & tough, or playful & light. A single default tone alienates a large share of users. Letting people curate how their coach talks to them turned a weakness (an AI can't "just know" your preferences) into an explicit, user-controlled strength.
Conversations save into categories the coach can reference — so it can surface patterns over time, the way a real mentor recalls "you've mentioned this same blocker three times." The direct answer to the continuity problem: a mentor is only valuable if it remembers.
Resolving flow logic in dialogue first meant Figma time went to finishing sound flows, not discovering problems mid-build — a process shift made possible by an AI collaborator that reasons through "what if" as fast as I can ask.
AI-era product design isn't just using AI tools faster — it's designing a new surface. A meaningful part of this work was defining how the AI itself should behave, not just what the screens look like. That's a skill set that didn't really exist five years ago, and one I now have real, shipped experience in.
Conversational prototyping changes the economics of exploring ideas. Testing flow logic in conversation meant bad ideas died in minutes instead of after a design review — so I could explore more of the problem space per week than a Figma-first process allows.
Personalization has to be explicit when the "person" is an AI. A human mentor adapts implicitly, over time. An AI coach has to be told — which meant designing direct, low-friction ways for users to shape their own experience, like tone selection, rather than hoping the product would infer it.