AI product systems / Intent to action
Wizzo
Designed and built an AI mentor product system that turns goals, blockers, context, and progress into one clear next move with reviewable follow-through.
- Operating proof
- AI product systems connecting intent, execution context, and accountable follow-through across Mentor, Campaign, Focus, and Vault surfaces
- Engagement
- Wizzo Labs
- Evidence set
- 2 reviewed artifacts
- Capability coverage
- 5 documented areas
Primary artifact
01 / 02Living Mentor workspace
Current authenticated product surface shown without personal content, pairing a focused mentor prompt, voice controls, quick starts, campaigns, and reviewable follow-through.
01 / Context
Situation
•Intent-to-Action Gap
AI conversations often stop at advice. Wizzo reframes that moment as an execution problem: how to turn goals, blockers, deadlines, and recent progress into next steps a user can actually finish.
•Product Context
The public beta positions Wizzo as an AI mentor for real-world follow-through: plans become quests, one next move stays visible, and the user reviews every meaningful action.
•System Context
The product needed to connect AI guidance with real work surfaces while keeping privacy, account controls, and product trust visible from the start.
02 / Mandate
Mandate
•Design an AI Mentor Loop
Create a product loop where the Mentor can capture intent, preserve execution context, suggest grounded next moves, and translate progress into quests and campaigns.
•Connect Work Context
Support Drive, Gmail, Calendar, and technical workflows so the AI mentor can reason from the artifacts and deadlines that shape real follow-through.
•Make AI Actions Grounded
Pair the Mentor with explicit, user-invoked tools and visible review points so assistance can move beyond generic coaching without pretending the AI owns the outcome.
•Preserve User Control
Build account export, confirmed deletion, and privacy-aware controls into the product system so personal progress data remains manageable.
03 / Build
Build
1.Product System Architecture
Designed the current experience around the Living Mentor, campaigns, focused next moves, progress visibility, and a Vault for account and trust controls.
2.Full-Stack Implementation
Built the product with Next.js, TypeScript, Postgres, Drizzle ORM, Neon, Vercel, OpenAI integrations, and Google workspace connectors.
3.AI Workflow Surfaces
Created mentor interactions for goal planning, progress reflection, guided focus, grounded research, connected notes, and voice-supported follow-through.
4.Trust and Account Controls
Added export and confirmed deletion paths covering product, AI, notification, integration, ML-derived, and community records.
Living Mentor workspace
Current authenticated product surface shown without personal content, pairing a focused mentor prompt, voice controls, quick starts, campaigns, and reviewable follow-through.
04 / Outcomes
Outcomes
•Public Beta
Shipped a public beta with a marketing site, live web app, Living Mentor, campaigns, focused next moves, quest progress, and connected work context.
•AI Product Proof
Demonstrated an AI-native product system that combines a mentor, campaigns, focused action, work connectors, explicit tools, privacy controls, and visible progress loops.
•Portfolio Relevance
Serves as proof of product design, engineering execution, AI workflow design, and operational thinking across a real shipped SaaS surface.
Public Wizzo product story
Current public product surface frames plans as quests, keeps one next action visible, and makes human review part of the product promise.
Continue the evidence trail
From proof to role fit
Compare Wizzo with adjacent systems, or carry its reviewed capabilities into an Adaptive Focus brief.