Hyder Shah

Why Afterbuild Labs exists

The story started with a pattern. Over the course of a year, Hyder kept getting the same message from non-technical founders: “I built something in Lovable and it works in preview, but I can't get it to work for real users.” The app would demo beautifully. Then the first paying customer would hit it, and something quiet and expensive would break — a Stripe webhook that never fired on subscription renewal, a Supabase table with Row Level Security still disabled from the defaults, a deploy pipeline that failed silently and left the last three commits unreleased.

These weren't exotic bugs. They were the same fifteen or twenty problems, showing up in a different order on every project. The founders weren't incompetent — they had shipped a real product in weeks that would have taken a traditional agency six months to quote. They just didn't know what they didn't know, and the AI tools had no way to tell them.

Hyder had spent a decade fixing those exact problems on production systems — payment flows at fintech startups, multi-tenant auth at B2B SaaS companies, deploy pipelines at infrastructure teams. The expertise existed. It just wasn't accessible at a price or timeframe a solo founder could stomach. Afterbuild Labs is the attempt to close that gap: senior production engineering, scoped in days rather than quarters, priced as a fixed fee so founders know the number before the work starts.

What makes AI-app rescue different

The biggest misconception about AI-generated code is that it's bad code. It usually isn't. Modern tools like Lovable, Bolt, Cursor, and Claude Code produce code that's readable, conventional, and often better-structured than what a junior engineer would write under deadline. The problem isn't quality. The problem is incompleteness.

AI tools are trained to produce working demos. They handle the happy path — the signed-in user, the valid form, the successful payment — with startling competence. What they systematically skip is the 20% of code that matters most in production: error boundaries, idempotency keys, Row Level Security policies, webhook signature verification, secret rotation, rollback procedures, rate limiting, input validation at trust boundaries, and the dozen other small disciplines that separate a prototype from a product.

Rescuing an AI-built app isn't a rewrite. It's a surgical audit. The skill isn't knowing how to build the app from scratch — the AI already did that. The skill is knowing, within minutes of reading the repo, which of those twenty failure modes are present and how to add the missing pieces without breaking what works. Most rescues touch less than 15% of the codebase. The other 85% stays exactly as the AI wrote it.

That's why Afterbuild Labs preserves rather than rewrites by default. A rewrite throws away a working product — the routes, the schema, the UI, and all the tiny product decisions the founder made while iterating. Those decisions are valuable. The goal is to add the production-grade layer underneath them, not to replace them with something the engineer would have written instead.

The anatomy of a broken AI app

After auditing dozens of AI-built apps in production, the failure modes cluster into four areas — and they show up in roughly the same order every time. Auth cracks first. Then the database leaks. Then payments quietly desync. Then a deploy goes sideways and nobody can tell whether the last commit actually shipped. Each of these has the same root cause: the AI builder was optimising for a working demo, not a working product, and the gap between those two things is where real users live.

Why auth breaks first.AI builders wire up the happy path with confidence — user signs up, user logs in, user sees their dashboard. What they miss is everything that surrounds that path: email verification flows where the token link actually works, password reset with sensible token expiry, session refresh on page reload so users don't get silently logged out, OAuth state parameter validation to prevent CSRF on the redirect, and logout that actually invalidates the session everywhere instead of just clearing a cookie on one device. When users report “I got logged out randomly” or “the password reset link doesn't work,” it's always one of those five gaps. Lovable in particular generates Supabase Auth calls but skips the edge cases — the helper functions exist, they're just never called from the right places. Typical fix time: 2–4 hours once you know what you're looking for.

Why Supabase RLS is the most dangerous gap.Every AI-built app on Supabase ships with Row Level Security disabled unless the developer explicitly enabled it — and AI tools rarely do, because it breaks the preview flow. Without RLS, any authenticated user can open the browser console and run SELECT * FROM ordersagainst your Supabase client and see every order in the database. Every customer's email, every payment amount, every shipping address. In 2024, security researchers scanning public Lovable apps found 170 production deployments leaking data on more than 18,000 users — every one of them because RLS had never been turned on. The fix is a set of row-level policies, one per table, typically five lines of SQL each. The hard part isn't writing the policies — it's finding every table that needs one, writing them in a way that doesn't silently break the queries the app already depends on, and testing that the enforcement actually holds under every role and every query path. Missing a single table leaves the whole thing leaking.

Why Stripe fails at scale. The Stripe demo works because the test flow only ever exercises a single webhook event: checkout.session.completed. That's the one the AI tool wired up, because that's the one the tutorial showed. Real production payments also depend on at least four others: invoice.paid for recurring billing cycles, customer.subscription.updated for plan changes and upgrades, customer.subscription.deleted for cancellations and churn, and payment_intent.payment_failed for dunning and retry logic. The AI builder wires one event. The other four exist in the Stripe dashboard but are silently ignored by the app. The result is the same pattern every time — a user pays, Stripe confirms the charge, and the app still shows them the free tier because no handler updated the database. This is the single biggest cause of founder panic in the first month after launch, and it's almost never noticed until a customer complains.

Why deploys are fragile. AI-generated deploy configs are optimistic — they describe the world the AI was trained on, not the world the app actually ships into. Environment variables get defined in .env.localbut never mirrored into Vercel's project settings, so the preview build works and production quietly runs on undefined keys. Webhook URLs are hardcoded to localhost:3000 and nobody remembers to swap them before the first customer hits production. Stripe test-mode keys end up in the production build because the build script doesn't distinguish environments. Proper deployment verification requires running the exact production environment locally, with production secrets and production URLs, and watching the logs while you exercise every payment and auth path — which is exactly what AI tools don't model. Until a human walks that path end-to-end, the deploy is a guess dressed up as a pipeline.

Known for

• Shipping Lovable prototypes into production — auth, billing, and deploys that stay up.
• Supabase RLS audits that actually cover the edge cases.
• Next.js App Router migrations off no-code and low-code stacks.
• AI-generated code audits — finding the bugs the model was never going to catch.
• Stripe integrations that survive failed renewals, disputes, and webhook retries.
• Incremental handoffs that leave in-house teams more capable than they started.