What to prepare before handing your AI-built product to an engineering partner
Prepare a useful engineering handoff for an AI-built product with paying customers. Bring the customer journey, system inventory, safe access and a clear outcome.
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More customers, fewer interruptions and a product that can grow. Practical guidance for deciding what your technology needs next.
Set usage limits that protect margins and keep customers happy
Design clear AI product allowances without surprise charges or duplicate usage. Align customer promises, server enforcement and reliable usage records.
Protect customer files from upload to download
Secure customer documents in an AI-built product. Review upload validation, workspace permissions, private downloads and safe processing with Supabase.
Keep AI answers useful as your customer base grows
Check AI output quality against real customer tasks. Build representative examples, separate format from accuracy and review changes before wider release.
A database backup only helps if you can restore the product
Check whether your AI-built product can recover from lost data. Test database restores, customer files, access and external systems before you need them.
Ship changes without breaking the product customers pay for
Make releases safer for an AI-built product with paying customers. Check critical journeys, control rollout and separate code rollback from data recovery.
Find and fix slow Supabase queries before upgrading your database
Trace slow Supabase screens to the real query. Review plans, indexes, permissions and pagination before paying for more database capacity or migrating.
Keep your AI product usable when API rate limits hit
Handle AI API limits without losing customer jobs. Plan fair queues, bounded retries and clear waiting states before a burst of demand turns into failures.
Stop duplicate requests from doing the same paid work twice
Prevent repeat clicks and retries from creating duplicate jobs, emails or charges. Learn where idempotency belongs and what to test in a paid AI product.
Keep long-running AI jobs working after a customer closes the tab
Make long AI tasks survive closed tabs, timeouts and retries. A practical guide to job status, durable results and recovery for paid AI products.
How to reduce AI API costs without making the product worse
Reduce AI API costs by finding repeated work, choosing models carefully and checking caching. Protect the result your paying customers rely on.
Supabase security checks before you add more customers
Check Supabase permissions, customer data boundaries and server keys before growth exposes a problem. A practical review for founders with paying customers.
Stripe webhooks: keep payments and access in sync
Paid customers should get reliable access. Check Stripe webhook retries, subscription rules and recovery before billing issues cost you customers.
Your AI-built MVP has customers. What comes next?
How to turn a working product into technology you can understand, operate and safely extend.
What does “ready to scale” actually mean?
A practical way to connect growth plans to capacity, reliability and the ability to make changes.
Find the bottleneck before changing the system
How to turn “the product feels slow” into a testable explanation and a focused engineering decision.
Make the product dependable before deciding to rewrite it
A practical sequence for reducing recurring failures and learning whether the architecture really needs replacement.
Modular monolith or microservices? Start with the boundaries.
How to decide whether independent services solve a real constraint for your product and team.
How to migrate a live product without losing what works
A practical framework for preserving behavior, moving data and proving the replacement before retiring the original.
Put the next question to work.
Six practical steps for founders whose customers are starting to outgrow the technology.
Read the growth guideAbout these notes
Practical thinking on scaling, reliability and making the right technical change. Each article connects a business concern to the engineering decisions behind it.
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