← AI App Building (No-Code & Low-Code)
Day 14 of 14
Putting It Together — Your AI App Architecture
The Reference Stack
After 14 days, here's the production-ready reference stack for a modern AI-powered app:
Frontend: Next.js (Vercel deployment) or SwiftUI (iOS native). Component library: shadcn/ui for web. State management: Zustand or React Query for web, SwiftData for iOS.
Backend/Infrastructure: Supabase (Postgres + Auth + Storage + Realtime + pgvector). Cloudflare Workers for edge functions and API endpoints that need global performance. Cloudflare R2 for file storage (cheaper than S3).
AI Layer: Claude API (Anthropic) for primary LLM calls. Voyage embeddings for semantic search and RAG. Prompt caching for cost optimisation. RAG over Supabase pgvector for domain knowledge.
Automation: n8n (self-hosted on Digital Ocean) for workflow automation. Supabase database webhooks as triggers. n8n for connecting Claude API calls to business workflows.
Integrations: Stripe + RevenueCat for payments and subscription management. Apple StoreKit 2 for in-app purchases. Xero API for accounting. ClickUp API for project management.
Monitoring: Sentry for error tracking. PostHog for product analytics. Cloudflare Analytics for traffic. ChartMogul for revenue metrics.
The Build Priority Framework
With this stack in mind, the build order for a new AI-powered feature: (1) Define the value proposition and user story (1 hour). (2) Design the data model in Supabase (30 minutes). (3) Build the core logic in a Cloudflare Worker or Next.js API route (2–4 hours with Cursor). (4) Build the UI (2–4 hours with Cursor + shadcn/ui). (5) Add the AI layer — Claude API call with system prompt, test on 20 real inputs (1–2 hours). (6) Add automation — n8n workflow for downstream actions (1 hour). (7) Deploy to Vercel preview, test, iterate. (8) Deploy to production with phased rollout.
Total time: 8–12 hours for a meaningful AI-powered feature. This is the power of this stack — it removes infrastructure friction and lets you focus entirely on the product logic.
For Darkice Interactive in 2024–2026: the three highest-leverage AI investments are: (1) BathCheck report generation (saves OTs 15 min/assessment, directly monetisable), (2) ShowerBuddy proactive risk detection (uses shower patterns to identify changes in behaviour, premium feature for carers), (3) Gliderol/Dormakaba predictive maintenance (access pattern analysis to predict door failures, enterprise upsell). Each is buildable in under 2 weeks with this stack.
⚡ Today's Action
Choose one AI feature you'll build in the next 30 days. Write the complete spec: the user story, the data model, the system prompt, the tech stack choices, the measurement plan, and the success criteria. Share it with someone technical for feedback. This becomes your sprint plan.
💡 Pro Tip
Every new AI feature should have a 'metrics from day one' requirement: which user action does this feature drive, how do we know if it's working, and what's the cost per use? Build the measurement in before launch, not as an afterthought.