Software made for keeps.
We build websites, web apps, and native iOS and macOS apps. AI handles a lot of the typing now. The time it saves goes to architecture, taste, and the bits where judgment actually matters. Some projects take a week, some a quarter, some are ongoing.
Better tools make exploration cheap. Knowing which directions are worth keeping is still the work.
- 01
Exploration is cheap. Judgment is not.
We can run several directions in parallel, faster than before. Experience picks the ones worth evolving.
- 02
Speed requires structure.
Working faster doesn't mean less process — it means more. We shape each exploration on purpose, so iteration produces understanding instead of noise.
- 03
Technology expands the search space.
The surface we can explore is wider now. Not just the browser — we iterate across the whole stack, and past it.
- 04
Strong ideas survive pressure.
Running many directions exposes the weak ones fast. The strong ones keep holding up as they're tested, expanded, and refined.
Our approach
Most web apps break in the unglamorous places: auth, state, payments, the session that expires at the worst moment. That's where we spend time. AI takes the parts it's fine at, like route handlers, boilerplate, migrations, and a scaffold of tests, so the load-bearing pieces get real attention.
The outcome
An app that holds up the first time a real user finds an edge case nobody thought of. Auth, state, and payments are done properly early, so the next feature sits on top of something solid instead of working around it.
What you get
- Full-stack build, database through UI
- An architecture that survives a pivot
- Auth, payments, and the load-bearing plumbing done properly
- CI/CD, observability, and alerting wired in from the first deploy
- A codebase your team inherits without an archaeology pass
Our approach
Native is discipline the web forgives. Hit targets, reachability, motion that knows when to stop, state that restores itself when you come back an hour later. AI saves time on view scaffolding and snapshot tests. We put that time into the details a phone actually rewards.
The outcome
An app that feels right before the user thinks about why. It shows up where the platform expects it: Spotlight, Shortcuts, Share Sheet, widgets, menu bar. When you hand it off or bring it in-house, the onboarding is boring instead of a week of archaeology.
What you get
- Native SwiftUI, shared codebase or per-platform
- System integrations: Shortcuts, Spotlight, Share Sheet, widgets, menu bar
- Accessibility and localization treated as part of the build, not a later pass
- App Store or direct distribution, signed and notarized
- Clean handoff: repo, TestFlight, CI, crash reports
Our approach
Most marketing sites have one job: turn a visit into a call, a signup, or a download. We spend the time on the path that does that, then let the rest of the site earn its keep. Your CMS ends up in the hands of whoever writes the copy, not in ours. Hosting stays boring.
The outcome
A site that looks like yours, loads fast, and says the thing. Your team edits copy without a deploy. Analytics are pointed at the signals that actually matter (calls, signups, downloads) and nothing else.
What you get
- Design and build tuned to your brand
- Structure shaped around the conversion path
- A CMS your team can actually edit
- SEO and structured data done properly
- Hosting, analytics, and a domain handled for you
Our approach
AI assistants are starting to use software on behalf of people, and your product's interface has to make room for them. We design the joints: how much the agent can do on its own, what it checks in on, what it announces before it runs, how it gets out of the way when a human wants the wheel back. The goal is a product both audiences can trust, without either one feeling like an afterthought.
The outcome
A product that reads clearly to a human and cleanly to a model. Agents work within scoped permissions. The interface shows what the agent is about to do before it does it. Humans can step in mid-flow and step back out again without anyone having to hit refresh.
What you get
- Scoped permissions model for agent actions
- A 'here's what I'm about to do' step before anything irreversible
- Readable logs and a show-your-work panel for what the agent did
- Escape hatches: the human takes the wheel, cleanly
- Patterns you can extend across the product as it grows
Our approach
Most teams have a list of things that would be great if they just happened: inbound triage, lead research, report assembly, the dozen Slack-to-Linear hops that eat a morning. We look at the list, find the parts where a model saves real hours instead of shaving minutes, and build small agents that do them with a human in reach. Boundaries are tight on purpose: one job, one scope, a log your team can read.
The outcome
Work that used to sit on someone's plate moves through the pipe by itself, with a record of what happened. Nothing irreversible fires without a human pass. When the model's wrong (and it will be), you see it quickly, you can turn it off quickly, and you can tune it without a rewrite.
What you get
- A map of the workflows worth automating, and the ones not worth it
- Agents scoped to one job, with readable logs
- Human checkpoints before anything irreversible
- Integrations with the tools you already run: Slack, Linear, email, your CRM
- A kill switch and a set of dials to tune the behavior
Our approach
First week is diagnosis: which parts are load-bearing, which are pretending to be, which ones blow up first. From there we stabilize the paths real users hit, add tests around them, and cut the modules that aren't earning their keep. The app stays live through the whole thing. We'll recommend a full rewrite when there's nothing worth keeping, which in practice is rare.
The outcome
A codebase your team can work on without wincing. Tests where they pay off, logging that points at the actual cause, and a ranked list of the risks that are left. Your roadmap stops hitting the 'nobody touches that file' wall.
What you get
- Diagnostic pass with risks ranked by blast radius
- Stabilization PRs your team can review and merge at their pace
- Tests on the paths that actually matter
- Logging, alerting, and error handling that point at the cause
- A short readme the next engineer will thank us for
Our approach
Half a week each month, pointed wherever it matters. New features, marketing pages, design polish, that one interaction your team never has time to get right. You open Linear tickets, we work the queue. When the queue is empty, we ship the things you meant to file but didn't.
The outcome
Capacity for the work that keeps getting pushed. The redesign no one has bandwidth for. The marketing page that's been 'almost done' for six weeks. The onboarding polish that matters but isn't anyone's sprint goal.
What you get
- Half a week of focused work, monthly
- Shared Linear, Figma, and repo access
- Weekly check-in, async otherwise
- Pause any month after the initial three
- A consistent eye on the small details
- Upfront
- 50% on kickoff, 50% on delivery
- Revisions
- Unlimited within scope
- Handoff
- You own the repo and the docs
Intro call
Thirty minutes. You talk about what you're building, we ask questions. If it's a fit, a short proposal follows that week.
Scope
One page. What we're making, when it's due, how we'll know it's done. No lawyer speak.
Build
We design and code in the same place. Every Friday you see what we made that week. Sometimes it's rough, that's fine.
Handoff
You get the repo, a walkthrough, and readable docs. We're reachable for thirty days after launch if something breaks.
Start a project.
A couple sentences works. What you're making, when you want it, what success looks like.
hello@eunoia.co