Orynix is six senior engineers. We build SaaS products, AI features, and the internal platforms that hold them together. Usually as the whole team, for several months at a time.
Six things we've done many times. We've turned down work outside this list more than once, because the projects we say yes to are the ones we can ship well.
We build SaaS products that scale past the founding team. Multi-tenant from the first version, with auth, billing, and an admin already in place.
The model is rarely the hard part. We spend most of the time on the interface, the eval set, and what should happen when the model is wrong.
Most internal tools start as a spreadsheet and never recover. We build the software ops, finance, and support teams actually want to open every day.
Scheduled pipelines that move data, files, and decisions. The retries actually work, and the dashboards make sense at 2am.
One team across the stack: design, frontend, backend, infrastructure. The same person who picks the database picks the empty state.
Integrations are boring until two systems disagree about what happened. We design for that day from the start.
What we were handed, what we shipped, and what changed for the team after we left.
Each phase has a definition of done you'll agree to before we start. The plan is a written document, not a Gantt chart. You can disagree with it line by line.
One week, with the people who actually own the outcome. We're trying to understand what's working, what isn't, and what you've already tried that didn't.
A written document covering architecture, scope, milestones, and an explicit list of what we're choosing not to build. The kind you can disagree with line by line.
We design in the actual stack, against real data. No Figma round-trip. Components live in the same repo as the code that ships them.
Small, frequent diffs. Code review on every change. Tests where they catch real bugs, instrumentation where it earns its keep.
Behind flags first, then a slow ramp while we watch the dashboards. We stay close for the first month, then hand over once your team is comfortable owning it.
None of these are clever. They're the patterns we've watched pay off across the projects we've shipped, and the ones we wish we'd applied harder on the projects we didn't.
The line of code we're proudest of when we write it is usually the one we rewrite a year later. We default to whatever the next engineer would expect to find, and we earn complexity only when there's a measurable reason to.
Latency, bundle size, and database response times shape what the product feels like to use. We pick budgets for them at the start of a project instead of treating them as cleanup work at the end.
Code gets read more often than it gets written. We optimize for the engineer who has to debug it on a Tuesday morning a year from now. Sometimes that engineer is us. Usually it's someone on your team.
Postgres, Redis, a job queue, and one of the major cloud providers. We spend our novelty budget on the parts of the product the user actually sees.
Two engineers replaced our entire billing rebuild plan. They were already deep in the code on day three, and the cutover was a non-event. That's not normal.
Honest about what AI could and couldn't do for us. We'd burned a year on a vendor who oversold; this team showed up with an eval set in week one and let the numbers do the talking.
I came in expecting a vendor and got an opinion. They turned down half the scope we proposed because it wouldn't have moved a number. They were right.
We inherited the codebase last month after the engagement ended. I've onboarded three teams worth of code in my career; this is the first one I haven't had questions about.
Send a paragraph or two and we'll write back within a business day. If it's a fit, we'll set up a 25-minute call. If it's not, we'll usually know someone better suited.