Updated
The data layer in Release Coach AI, Inspire Film and Reveal Software sits on PostgreSQL. In the products where we use Supabase, PostgreSQL is underneath there too.
Start with the product’s records, relationships and access boundaries. Define representative queries, data changes and recovery requirements before agreeing a database scope. The cost of changing a model depends on the actual data and application; there is no universal week-versus-month comparison with interface work.
AI can produce a schema that works, but it does not know where the product is heading in two years. Tenant separation, soft deletes, currency fields and which time zone your timestamps are stored in are the decisions that are hardest to correct later. An engineer designs these tables, and generated migration files are read line by line before they are merged. This is the one part of the stack where being slow up front is genuinely cheap.
Reveal’s case documents PostgreSQL behind connected web, extension and administration surfaces. It does not document a particular tenant-isolation policy. For a new product, define account boundaries, where access is checked and the operations that need cross-account rejection tests. Database-level controls are not evidence that every later query is automatically safe.
Use representative queries and measured behaviour to identify database work. Review the relevant schema, access patterns and query plans before proposing indexes or other changes. An ORM does not establish a performance result, and a slow screen does not by itself prove that a missing index is the cause. Agree checks that compare the intended workflow before and after the change.
Taking a backup is not enough. Until you have tried restoring one, you do not know whether you have a backup at all. At handover we leave the recovery steps in writing. Migrations being reversible matters just as much, because a bad migration is one of the fastest ways to do damage in production, and the middle of an incident is a poor time to work out how to undo it.
Senior engineers building your product with AI coding agents: web, mobile, SaaS or browser extension, reviewed before anything ships.
AI-accelerated development, billed monthly. We confirm scope, capacity and start date in writing before checkout.
Compare the actual records, relationships, query patterns and operating requirements. Review a representative workflow and its data changes before choosing a database model. A generic prediction about when a schemaless product will need relationships is not evidence for your project.
Yes. We work through the schema, indexes, slow queries and access permissions, then produce written recommendations. Work runs as AI-accelerated development, starting from $2,000 per month, with scope agreed in writing.
Migrations are kept in version control and run against a copy before they reach production. We do not merge a migration that has no rollback step, regardless of how straightforward it looks at the time of writing.
Yes. Infrastructure is set up in your accounts and ownership stays with you. We do not build structures that create a dependency on us after handover, because that is not a healthy basis for a partnership.
One paragraph on what you are building, roughly when you need it, and who it is for. No brief, no form with fifteen fields.
From the engineer who would do the work, not a sales team. If we are not the right fit we say so, and where possible we point you somewhere better.
Scope, price and timeline in writing before anything is committed. You decide with the numbers in front of you.
No long contract to begin, no lock-in to our tooling, and the code is yours from the first commit.
Start a conversationA short note about the product, the timeline and who it is for is enough to start. You will hear back from the engineer who would do the work, not a sales team.
Ankara / Türkiye · Working across European and US time zones
