How We Used Agentic Workflows and AI Automation Without Risking Core Transactions

We separated AI-enhanced workflows from mission-critical transaction paths. In this codebase, AI accelerates media generation and sprint execution, while booking, billing, and authorization remain deterministic through Supabase RLS, middleware controls, and Stripe-verified server events. That separation preserves reliability and auditability under growth pressure.

Where AI appears in this project

1. Delivery workflow: sprint-planning artifacts show iterative, agent-assisted development cycles.
2. Operational automation: a script uses OpenAI image generation to fill missing service/product visuals and publish them to Supabase Storage.

AI automation snippet

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

const imageResponse = await openai.images.generate({
  model: "dall-e-3",
  prompt,
  size: "1024x1024",
  quality: "standard",
  n: 1,
});

const { error: updateError } = await supabase
  .from("services")
  .update({ image_url: publicUrl })
  .eq("id", service.id);

Guardrail design principle

AI should optimize content velocity and development throughput, not own your financial or access-control truth. In COOARD, Stripe events, middleware role checks, and SQL policies remain the final authority.

Why this matters in 2026

GEO and AI search reward transparent engineering narratives. Showing where AI is used and where it is intentionally not used improves trust, especially for operations-heavy SaaS categories.

Read the full implementation context in the case study: /case-studies/cooard-salon-platform