300 lines
3.6 KiB
Markdown
300 lines
3.6 KiB
Markdown
Google Cloud Product OS
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Product-Centric IDE + SaaS Autopilot Platform (Requirements & Architecture)
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Vision
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Build a Product-Centric IDE and Automation Platform dedicated exclusively to:
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Launching, growing, and operating SaaS products on Google Cloud
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This is NOT a general-purpose IDE.
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This is a Product Operating System (Product OS) designed to unify:
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Code
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Marketing
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Analytics
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Growth
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Support
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Experiments
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Infrastructure
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AI-driven automation
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into one coherent platform.
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It delivers:
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A Cursor-like experience
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Without Cursor cost
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Powered by Gemini (Vertex AI)
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Optimized specifically for Google Cloud
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Focused exclusively on building & automating products
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Core Product Principles
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1. Product-Centric, Not Code-Centric
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This platform optimizes for:
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Shipping, launching, growing, and optimizing products, not just writing code.
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2. Opinionated for Google Cloud
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This system is:
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Cloud Run-first
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Firestore / Cloud SQL-native
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BigQuery-native
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Cloud Build-native
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Gemini-native
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No AWS, no Azure, no multi-cloud abstraction.
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3. Automation First
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Everything is:
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Automatable
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Observable
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Auditable
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Optimizable
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4. AI as a Product Operator
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The AI is not just a coding assistant.
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It is a:
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Product Operator AI
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capable of coordinating marketing, growth, support, analytics, and code.
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IDE Structure: Product-Centric Layout
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Instead of a traditional IDE layout, the system must expose:
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Product OS
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├── Code
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├── Marketing
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├── Analytics
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├── Growth
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├── Support
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├── Experiments
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└── Infrastructure
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Each section is first-class and AI-assisted.
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Section Requirements
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1. Code Section
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Purpose:
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Build and deploy product services
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Must support:
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Cloud Run services
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Cloud SQL / Firestore integration
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Secrets management
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Logs & traces
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Rollbacks
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Service templates
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Not required:
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Arbitrary framework support
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Every programming language
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Optimized languages:
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TypeScript / Node
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Python
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2. Marketing Section
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Purpose:
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Automate go-to-market and content execution
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Must support:
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Campaign generation
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Social scheduling (Missinglettr)
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Blog generation & updates
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Landing page updates
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Brand voice control
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Product update → campaign pipeline
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AI must:
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Convert product changes into launch content
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Adapt content to brand style
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3. Analytics Section
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Purpose:
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Understand product performance and causality
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Must support:
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Funnels
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Retention
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Activation
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Cohorts
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LTV
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Causal drivers
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Experiment results
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NOT a SQL editor.
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This is a Product Intelligence Interface.
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AI must answer:
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"Why did conversion change?"
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"What caused activation to drop?"
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"What should we test next?"
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4. Growth Section
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Purpose:
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Optimize onboarding and conversion
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Must support:
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Funnel definitions
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Onboarding flows
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Growth experiments
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A/B tests
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Nudge systems
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Conversion optimization
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AI must:
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Detect drop-offs
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Recommend experiments
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Evaluate uplift
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5. Support Section
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Purpose:
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Integrate customer feedback and product health
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Must support:
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Ticket ingestion
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AI-assisted replies
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Knowledge base generation
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Product issue detection
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Issue → fix pipeline
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AI must:
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Generate replies
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Detect recurring issues
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Recommend fixes
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6. Experiments Section
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Purpose:
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Coordinate A/B tests and product experiments
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Must support:
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Experiment definitions
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Targeting
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Metrics tracking
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Statistical significance
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Rollout controls
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AI must:
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Suggest experiments
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Analyze results
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Recommend actions
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7. Infrastructure Section
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Purpose:
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Manage and monitor production systems
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Must support:
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Cloud Run deployments
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Firestore / Cloud SQL management
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Secrets
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Logs
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Traces
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Alerts
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Cost monitoring
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AI must:
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Detect anomalies
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Recommend optimizations
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Automate fixes
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