Supabase development
Supabase Development Services
When a workflow outgrows spreadsheets and no-code tables, it usually needs a real database rather than a bigger no-code plan. Supabase gives you Postgres with an API, auth and storage in front of it, which is a short path from prototype to something you can build on.
What we build
Schema design
Tables, relationships and constraints designed once, properly, so the database refuses bad data rather than storing it and surfacing it later.
Row-level security
Access rules enforced in the database rather than in each application that talks to it — the part most projects postpone and then cannot retrofit cleanly.
Auth, storage and APIs
Users, files and endpoints wired together, so an automation or a front end can read and write without a bespoke backend in between.
Vector search for AI features
pgvector in the same database as the rest of the data, so retrieval for an AI agent does not need a second system to keep in step.
Where it stops
Every tool has limits worth knowing before you build on it. These are the ones that change a decision.
- It is Postgres, which means it rewards knowing Postgres. A badly designed schema is just as slow here as anywhere else, and the platform will not rescue it.
- Row-level security is powerful and unforgiving. Rules written casually either leak data or lock everyone out, and both failures are quiet.
- There is no visual editor for business users. Anyone who needs to browse and edit records directly needs an interface built for them.
- Connection limits matter under load. An automation platform opening a connection per run will hit them long before the database itself is stressed.
Worth reading first
How to share credentials safely, by credential type
The safest handover moves no secret at all. Which access to grant, which secrets must travel, how to send those, and what to revoke afterwards.
How to build an AI agent that answers customer questions
A step-by-step method: what it may answer, where the facts come from, how it hands over to a person, and how to know it is right before you launch.
Supabase FAQ
- When does a workflow need a real database?
- When correctness starts mattering more than convenience. The usual signals are volume that makes a no-code table slow, relationships a spreadsheet cannot express without duplication, and the need to guarantee that a record cannot exist in an invalid state. A database enforces those rules itself; every other option relies on the process around it being followed, which it eventually will not be.
- Supabase or Firebase?
- Supabase when the data is relational and you want SQL — which covers most business systems, because business data is relational whether or not it is stored that way. Firebase when the shape is document-oriented and the priority is real-time sync at scale. The practical difference is that Postgres skills and SQL transfer anywhere, while a document model is a decision that is expensive to reverse later.
- Can Supabase back an AI agent's memory?
- Yes, and keeping it in the same database as everything else is the point. With pgvector you store the embeddings alongside the records they relate to, so retrieval can filter by ordinary columns — this customer, this date range — before it searches semantically. Splitting the vectors into a separate service means keeping two systems in step, which is a problem you do not have to create.
- Is it safe to expose Supabase directly to a front end?
- Only with row-level security written and tested first. The API is generated from the schema, so anything the policies allow is reachable — which is exactly the intended design and also exactly how data gets exposed when the policies were treated as a later task. We write and test them as part of the schema, not after it.
Need help automating a process?
Tell us about the repetitive work on your team—we’ll map a practical custom automation plan.