AI-native SaaS, built to scale
In short
What is AI-native SaaS development?
AI-native SaaS development builds AI into the core of a product instead of adding a chatbot on the side. Onpolar builds multi-tenant SaaS on Go and PostgreSQL behind one OpenAPI contract, with assistants that only see each customer’s own data, tenant isolation enforced in the database, per-seat billing and a full audit trail.
- Typical timeline
- 2–3 months to production
- Core stack
- Go · PostgreSQL · OpenAPI · React
- Built this way
- ERPFlow.ai · Deal.ee
Updated
Products
SaaS products live in production.
What you get
Secure multi-tenancy
Each customer’s data walled off in the database itself, not just in the app.
AI that respects permissions
Assistants and agents that only read what the person asking is allowed to see.
Per-seat billing
Stripe subscriptions with seats, plan changes and proration handled.
An audit trail
Every change tied to a user and a customer, so support questions have answers.
How it works
- 01
Spec first
The API contract and the tenancy model written down before any code.
- 02
Foundations
Logins, roles, billing and the audit trail built before the features.
- 03
Features and AI
Product screens and the AI layer on top, then handed over with runbooks.
Compared
Production SaaS vs a prototype
| Compared on | Production SaaS | Prototype |
|---|---|---|
| Customer data | Isolated in the database | A filter someone can forget |
| The AI | Sees only what the user may see | Sees everything |
| Billing | Per seat, with plan changes | One price, one plan |
| Audit trail | Every change recorded | Application logs |
Questions
How long does it take to build a multi-tenant SaaS?
Two to three months to a production release for a focused product. ERPFlow, with invoicing, CRM, inventory, finance and reporting, Peppol e-invoicing and per-seat Stripe billing, took two months. Integrations and compliance scope move that number more than the feature count does.
What does AI-native mean in a SaaS product?
The AI reads your customers’ real records under the same permissions as the user asking, and can take actions that change data. It is not a chat box over public documentation. Onpolar routes models through a gateway, so changing AI provider is a configuration change rather than a rewrite.
Why Go and PostgreSQL rather than a JavaScript backend?
Predictable speed, real concurrency and a small deployment footprint, on a database whose row-level security can enforce tenant isolation below the application. TypeScript still runs the front end, with Next.js where server-side rendering earns its place.
Do we own the code?
Yes. The repository, the infrastructure definitions and the credentials are yours from the first commit, in your own accounts. There is no proprietary runtime and no licence to keep paying Onpolar for.
Can you take over an existing SaaS codebase?
Often, yes. Onpolar starts with an architecture review and a written assessment of the tenancy model, the login layer and the migration history before proposing any work. If a rebuild would cost less than a rescue, we say so.