Service lane
Web, apps & backend
AI needs a front door, and everything behind it. We build the product around the model: the interface people use, the backend that holds it up, the databases, real-time, and admin it runs on. With QA that actually watches, not just a green checkmark.
How it works
01
We design the product
From user flows down to the database and the API.
02
We build it full-stack
Real-time, offline-first, admin panels, one coherent build.
03
QA watches before it ships
Tests and reviews catch problems before your users do.
01
What it is
This is the software around the AI: the part people actually see and use, and the machinery underneath. Front-end interfaces, back-end services, databases, authentication, real-time and offline-first behaviour, admin panels to run it all. It turns a capable model into a product someone can open and rely on. QA watches what rendered on screen, not only whether the code ran.
02
What it's good for
A few shapes this takes in practice:
- The AI you built needs a real interface: something people can open, use, and trust, not a script only you can run.
- The work has to keep going when the connection doesn't: offline-first, syncing back up the moment it can.
- Several people need to see the same thing change at once. Real-time, without a refresh.
- Someone has to run it day to day: an admin panel to see, edit, and manage what's happening without touching the code.
03
What's possible
Full-stack builds front to back: web and app interfaces, back-end services and APIs, databases and data modelling, authentication and roles. Real-time via WebSocket, offline-first that survives a dropped connection, admin panels and internal tools, e-commerce and payment flows. QA woven through, including perceptive visual checks that catch the glitches a passing test suite misses. That kind of watching is what we also turn into observant agents.
04
What you gain
- A product, not a prototype. Built to be used by people who didn't write it.
- It holds up under real use: real-time, offline, and edge cases handled instead of hoped past.
- Fewer defects reach users, because QA watches the rendered result, not just the exit code.
- One team for the whole stack: front, back, and the AI in between, built and run together.
05
Who it's for
Anyone who has the AI working but needs a product around it people can actually use. Teams that need real-time or offline-first behaviour the usual build won't give them. Businesses that need a front door plus the admin, auth, and database behind it. And anyone tired of demos that pass tests but break on screen. If it has to work for real users, not just in a notebook, this is the lane.
06
How we build it
We build the whole stack as one: interface, backend, database, and the AI between them, starting from what the product has to do for the people using it. Real-time, offline-first, auth, and admin come in as the work needs them, not bolted on later. QA runs throughout, watching the rendered result on real screen sizes. The same people build it and run it, so it stays shipped: maintained, watched, and fixed when reality finds the edge case.
07
Examples
Illustrative, not case studies. Every build is scoped to what your product actually has to do:
- A real-time app several people use at once, each seeing the others' changes the instant they happen.
- An offline-first tool for a place with an unreliable connection. It keeps working, then syncs the moment it's back.
- An admin panel and backend behind an AI feature, so a team can run it day to day without touching the code.
- A visual-QA pass turned into an agent that watches for the glitches and layout breaks a green test run walks right past.
Want this wired into your stack?
OTW builds and runs it end to end. Join the waitlist for early access, or write to us and we'll work out what to wire first.