How we work

Awna's AI agents build the way Zia Saidi builds. Her standards for architecture, testing, code quality, security, databases and delivery are written down as a rulebook and a library of more than 30 skills, and the agents follow them on every task. Zia has spent 30 years taking software from idea to running system, and that experience is what the agents work from.

Because AI makes prototypes cheap, there's no long run of upfront phases. Plans are tested against working software from the first week, and the loop below runs until launch, and after it.

The practice keeps learning. When Zia corrects something in review, the correction becomes a pattern, and a pattern that recurs is written back into the standards. The team that builds your month-six feature is sharper than the one that built week one.

The loop

1. Discover and frame. We start with your goals, constraints, data and budget. Every open decision is raised and settled up front, so the agents work from complete plans and nothing stalls mid-build.
2. Prototype early. A working, clickable slice in days, not a slide deck, so we confirm the direction before building in depth.
3. Build in parallel. Independent pieces are built side by side by AI agents in separate branches, each following the written standards, then brought together and reviewed by Zia.
4. Verify. Changes are built test-first and checked green before they merge. Logic that handles money or critical data is tested against worked examples and a running system, not only mocks. Nothing reaches you until Zia has reviewed it: passing tests are the floor, not the sign-off.
5. Ship and iterate. Small releases land continuously, and written project notes carry the context between sessions, so month six moves as fast as week one. Then the loop repeats.

What makes it hold up

• A written operating system: verification before claims, test-first by default, one change per commit, and a check that runs before anything merges.
• More than 30 written skills that hold the standards for each part of the work.
• A team of AI agents with set roles, including a planner, an implementer, an independent reviewer and an asset generator, each filled by the model family best suited to it, so a review never shares the author's blind spots.
• A person signs off every delivery, and every correction goes back into the standards.
• Right-sized models: the most capable models where judgment matters, faster ones for mechanical work.
• Proven across stacks: TypeScript and NestJS, Vue, Go, Ruby on Rails, PHP and Laravel, R and Shiny, Python and FastAPI.

After launch

Launch is where the loop starts paying off. The same process keeps running:

• Security and dependency updates, kept current rather than put off.
• Fixes and new features, built test-first and reviewed.
• Performance and error monitoring, so problems surface before your users report them.

Because the standards and your project's history are written down, picking the work back up after three quiet months costs no ramp-up.

Talk to us

Email hello@awna.ai, or book a 30-minute intro call.