Built by a team shipping its own SaaS, not just other people's.
Multi-tenant architecture, AI features that survive production, deployment pipelines and inference cost control, decisions we've made with our own customers and our own money on the line.
What we've learned building for saas.
We operate Outlia and Setta as genuine multi-tenant products in production. That changes the advice we give, because we've paid for the mistakes: tenant isolation that leaked, inference bills that scaled faster than revenue, deployments that required a specific person to be awake.
The three things that most often need fixing in an existing SaaS codebase are isolation enforced in application code instead of the database, AI features with no evaluation harness so nobody knows whether a change helped, and a deploy process that discourages shipping.
We'll also tell you when multi-tenant is the wrong architecture for your buyers. Enterprise and regulated customers frequently want their own instance, and per-client deployment is a legitimate answer rather than a downgrade.
Where the money leaks.
Tenant isolation lives in application code
One missing filter away from returning another customer's data. It belongs in the database, enforced by row-level security.
AI features with no evaluation
Prompt and model changes ship on vibes because there's no harness measuring whether quality moved. Then costs rise and nobody can say why.
Shipping is an event
If deploying needs a quiet afternoon and a specific engineer, every other decision slows down around it.
The services that apply here.
SaaS Platforms
Auth, tenant isolation, billing, admin tooling, deployment and the operational work after launch. We've built …
AI-Native Product Development
There's a real architectural difference between a product with an AI feature and a product designed around mod…
DevOps Engineering
CI/CD, containerisation, environment separation and rollback. The goal is that shipping stops being a schedule…
Cloud Infrastructure
Architecture, migration and ongoing cost control, without the over-provisioned enterprise setup that a team yo…
SaaS-specific questions.
Can you audit our existing architecture?
Yes, and it's usually the right first step. An honest audit before anyone commits to a timeline beats a rewrite proposed on incomplete information.
How do you control AI inference costs?
Model routing and caching. Most production workloads have a long tail of simple requests that don't need a frontier model, separating those out is typically the largest single cost lever.
Do you take over maintenance?
On a retainer, yes. Or we hand over with documentation if you have an in-house team, we'd rather you not be dependent on us.
Work in saas?
Tell us what's actually breaking. If we're not the right fit we'll say so on the first call.