Product engineering
No six-month spec and a stage-gate. We use current AI tooling to go from spike to something customers keep while the question is still warm.
01 / Studio
We write software
we stand behind.
Dwarsoft is an AI-native product studio. We embed in your repo, ship with the tools of this decade — and stay with the product after it goes live. Same crew, still on the work.
02 / Practices
Four ways we show up.
A small set of practices we actually staff.
Platform & runtime
The boring path done well: deploys, observability, data stores, and the glue that lets a small team ship without heroics.
Interface systems
CLIs, dashboards, and SDKs treated as one language. Built for people who live in a keyboard, not a mood board.
Applied AI
Agents, evals, and tool-use inside the product — and in the repo while we write it. Intelligence in the loop, not a slide at the kickoff.
03 / Ship log
Selected work.
We publish the outcome, the constraint, and what we would not do again.
04 / Method
We join your team. We stay after it ships.
A small named crew in your tools, shipping work you can put in front of people. When the product is live, those names are still on it.
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01
Embed
Access, stand-ups, incident channel. We work where your team already works.
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02
Ship
Thin slices, production first. AI in the loop so we move while old shops are still writing the brief.
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03
Stay
Runbooks, pairing, and a named crew that stays on the line. Support is part of the contract, not an extra invoice after the applause.
05 / Voices
They hired us once. They kept us on.
A few words from people we still support.
They shipped the catalog, then they stayed on the bugs that only show up when a real pack hits the dock. That is the difference.
Retailers do not wait for a pretty release. Dwarsoft shipped the order path, then they stayed when a city went live and the catalog got ugly.
People drop off when they cannot see what is on today. Dwarsoft rebuilt the booking path, then they stayed when a Saturday slot list broke.
06 / Journal
Studio notes.
A first draft while the problem is still warm
The question that brought you here will cool. We try to have something in the repo before it does.
Ship logs, not slide decks
A sprint is what landed in main. The deck is optional. The log is not.
Evals before anyone sees a demo
A model that works in a slide is not a product. We do not show it until it fails on your real corpus.
07 / Next
Bring the messy problem.
If the product has outgrown the first architecture — or never had one — send a brief. We will have a first draft shipped while the problem is still warm.
Open a brief