Google Cloud Product OS Product-Centric IDE + SaaS Autopilot Platform (Requirements & Architecture) Vision Build a Product-Centric IDE and Automation Platform dedicated exclusively to: Launching, growing, and operating SaaS products on Google Cloud This is NOT a general-purpose IDE. This is a Product Operating System (Product OS) designed to unify: Code Marketing Analytics Growth Support Experiments Infrastructure AI-driven automation into one coherent platform. It delivers: A Cursor-like experience Without Cursor cost Powered by Gemini (Vertex AI) Optimized specifically for Google Cloud Focused exclusively on building & automating products Core Product Principles 1. Product-Centric, Not Code-Centric This platform optimizes for: Shipping, launching, growing, and optimizing products, not just writing code. 2. Opinionated for Google Cloud This system is: Cloud Run-first Firestore / Cloud SQL-native BigQuery-native Cloud Build-native Gemini-native No AWS, no Azure, no multi-cloud abstraction. 3. Automation First Everything is: Automatable Observable Auditable Optimizable 4. AI as a Product Operator The AI is not just a coding assistant. It is a: Product Operator AI capable of coordinating marketing, growth, support, analytics, and code. IDE Structure: Product-Centric Layout Instead of a traditional IDE layout, the system must expose: Product OS ├── Code ├── Marketing ├── Analytics ├── Growth ├── Support ├── Experiments └── Infrastructure Each section is first-class and AI-assisted. Section Requirements 1. Code Section Purpose: Build and deploy product services Must support: Cloud Run services Cloud SQL / Firestore integration Secrets management Logs & traces Rollbacks Service templates Not required: Arbitrary framework support Every programming language Optimized languages: TypeScript / Node Python 2. Marketing Section Purpose: Automate go-to-market and content execution Must support: Campaign generation Social scheduling (Missinglettr) Blog generation & updates Landing page updates Brand voice control Product update → campaign pipeline AI must: Convert product changes into launch content Adapt content to brand style 3. Analytics Section Purpose: Understand product performance and causality Must support: Funnels Retention Activation Cohorts LTV Causal drivers Experiment results NOT a SQL editor. This is a Product Intelligence Interface. AI must answer: “Why did conversion change?” “What caused activation to drop?” “What should we test next?” 4. Growth Section Purpose: Optimize onboarding and conversion Must support: Funnel definitions Onboarding flows Growth experiments A/B tests Nudge systems Conversion optimization AI must: Detect drop-offs Recommend experiments Evaluate uplift 5. Support Section Purpose: Integrate customer feedback and product health Must support: Ticket ingestion AI-assisted replies Knowledge base generation Product issue detection Feedback loops into product & marketing 6. Experiments Section Purpose: Enable continuous product optimization Must support: Hypothesis creation Experiment creation Assignment Result analysis Causal impact estimation Recommendation engine 7. Infrastructure Section Purpose: Hide GCP complexity behind product workflows Must support: Cloud Run provisioning Pub/Sub Cloud SQL / Firestore IAM abstraction Deploy / rollback Resource health No raw IAM or Terraform exposure by default. Everything should be expressed as product-level actions. AI System Design Supervisor AI (Product Operator) This is NOT a coding agent. It is a: Product Operator AI capable of coordinating decisions across: Marketing Growth Product Support Analytics Experiments Responsibilities: Interpret product goals Prioritize actions Dispatch tasks to tools Enforce policies Learn from outcomes Tool Execution Model (Critical Design Decision) Backend Tool Execution (Option 1) All tools execute on the backend. The IDE: NEVER runs gcloud NEVER holds cloud credentials NEVER touches databases directly Instead: IDE / Agent → Control Plane API → Executors → GCP Services Benefits: Security Auditing Shared automation with SaaS autopilot Centralized policy enforcement No local cloud configuration Control Plane Architecture Control Plane API A Cloud Run service responsible for: Authentication Tool registry Tool invocation routing Policy enforcement Run tracking Artifact storage (GCS) Gemini proxy Core endpoints: POST /tools/invoke GET /runs/{id} GET /runs/{id}/logs GET /tools GET /artifacts/{run_id} Tool Registry All actions are formalized as tools. Example: cloudrun.deploy_service analytics.get_funnel_summary firestore.update_company_brain missinglettr.publish_campaign experiments.create_ab_test Each tool defines: Input schema Output schema Risk level Executor mapping Used by: IDE Supervisor AI Web Dashboard Executors (Domain Services) Each executor is a Cloud Run service with scoped permissions. Deploy Executor Cloud Build Cloud Run Artifact Registry Analytics Executor BigQuery Causality modeling Funnel analysis Firestore Executor Company Brain Styles Configs SQL Executor Summaries from Cloud SQL Read-heavy Missinglettr Executor Campaign publishing Scheduling Data Layer Firestore Company Brain Style profiles Tool registry Policy configs Run metadata GCS Logs Artifacts AI outputs Generated patches Trace data BigQuery Events Causality models Experiments Analytics warehouse AI Code Editing Strategy We do NOT build a new editor. We use: VS Code APIs Patch-based updates Flow: AI generates structured diffs IDE previews changes User approves IDE applies locally Backend executes deploy/test Later: Backend can open PRs automatically IDE Base Technology Editor Base We use: ✅ VSCodium Not Code-OSS directly. Reasons: Open source OpenVSX marketplace Low maintenance Redistributable Fast to ship Language Strategy We support only: TypeScript / Node Python This allows: Better templates Better debugging Better automation Faster AI alignment IAM Strategy Users OAuth only No GCP IAM exposure Backend Service Accounts Least privilege Per-executor roles No key files Workload identity only Product vs General IDE: Explicit Non-Goals This platform is NOT: A general code editor A multi-cloud IDE A framework playground A replacement for VS Code for all use cases It IS: A Product Operating System A SaaS automation platform A GCP-native product launcher An AI-driven product operator Target Users Solo founders Indie hackers Startup teams AI-first SaaS companies Product-led growth teams Strategic Differentiation You are not competing with: VS Code Cursor JetBrains You are competing with: 10+ disconnected tools: Segment HubSpot Mixpanel Amplitude Intercom Zapier Notion Google Cloud Console Marketing schedulers Experiment platforms You replace them with: One Product Operating System Build Roadmap Phase 1: Core Platform Control Plane API Deploy Executor VSCodium Extension (Deploy + Logs) Gemini integration Phase 2: Product Intelligence Firestore Executor (Company Brain) Analytics Executor Funnel + driver tools Phase 3: Automation Marketing Executor Growth + Experimentation Supervisor AI Phase 4: Full Autopilot Approval workflows PR automation Continuous optimization Multi-tenant SaaS Final Statement This platform exists to enable: One-click product launch, AI-driven growth, and autonomous SaaS operation on Google Cloud. It is: A Product OS An AI Product Operator A Cursor-like experience A GCP-native automation platform