Every project we run follows the same four stages: discover, design, build and launch. The stages are familiar to anyone who has worked on software. What is different is that each one ends with something concrete you approve, and that AI does much of the repetitive work inside each stage while people make the decisions.
This page walks through each stage in detail. If you want to know how the work is priced, read pricing and estimates. If you want to know how we use AI, read AI-accelerated development.
-
1
Discover
A call, a short workshop and research. You receive a written scope, a plan and a fixed-scope estimate for the first milestone.
-
2
Design
User flows, wireframes and clickable prototypes. You approve the screens before heavy development starts.
-
3
Build
Working releases in short cycles. Every change is reviewed and tested. You test real features as they arrive.
-
4
Launch
Deployment, data migration, monitoring and training. You receive the code, documentation and access.
-
5
Support and grow
Fixes in the first weeks, then ongoing development or a clean handover to your team.
1. Discover
Discovery starts with a call about your goals, your users and the problem you want to solve. We ask a lot of questions. Who will use this? What do they do today? What would make this project a success in six months? What are the constraints: budget, deadlines, existing systems, rules?
For larger projects we follow the call with a short workshop and some research. AI helps here by summarizing competitor products, your existing documents and interview notes, and by drafting first user stories. People decide what matters.
You receive a written scope: what we will build, what we will not build yet, the main risks and assumptions, and a plan broken into milestones. The first milestone comes with a fixed-scope estimate, so you know exactly what you are committing to before any development starts.
2. Design
Design is where most expensive mistakes are prevented. We map the flows users will follow, sketch the screens, and turn them into clickable prototypes you can try on your own phone and laptop. It is much cheaper to move a button in a prototype than in finished software.
AI speeds up layout variations and first drafts of copy, and helps check designs against accessibility guidelines. Designers make the calls on how the product should feel, what to leave out and how it fits your brand. You approve the prototype before we move to building those screens.
At the same time, engineers design the parts you do not see: the data model, the integrations, hosting and security. These decisions are hard to change later, so they get careful attention and a written record.
3. Build
We build in short cycles, often called a sprint, each ending with working software you can use in a test environment. You do not wait until the end to see progress, and you can change priorities between cycles when you learn something new.
This is where AI saves the most time. It drafts screens, API endpoints, database queries, tests and documentation. Every draft is read, tested and approved by an engineer before it is merged. Automated tests run on every change. Nothing reaches you that a person has not checked. Read how we check quality for the details.
We keep you informed in writing as the work moves: what was finished, what is next, and any problems or decisions that need you. If something turns out harder than expected, you hear about it when we find it, along with options.
4. Launch
Launch is planned, not improvised. We set up production hosting and monitoring, move or import data, configure domains and redirects, and run a final round of testing on the live environment. For products replacing an old system, we often run both side by side for a period.
After launch we watch closely: errors, speed, and how people actually use the product. Issues found in the first weeks are fixed quickly. You receive the source code, documentation, and access to every account and service in your name.
How the pieces fit together
The diagram below shows how work moves through a typical project and where AI and people each contribute.
What you need to bring
A successful project needs a few things from your side. Someone who can make decisions and answer questions within a day or two. Access to the people who will use the product, even for a few short conversations. Existing materials such as documents, data samples, brand files and logins to current systems. And honest feedback on each release.
If you are preparing a brief, our guide on writing a requirements document helps, and so does how to choose a development partner. Many projects start small with an MVP and grow from there.