AIPT as a case study
Need a product like this?
AI Powered Testing was designed and built by one engineer using AI-assisted development, in a few months. A macOS app, a backend on AWS, a web console, AI integration and billing are parts of one production system. If your business needs its own product, an internal system or an AI-powered service, the same approach takes it from an idea to a working system — without assembling a full team first.
AIPT status, September 2026: version 1.4, Stripe payments going live
git log · wc -l · 2026-09-03
This product is the proof
One product. One engineer. Full stack.
Not a prototype and not a single app: tests run on the engineer's machine, scenarios and results live in the cloud, and Claude writes the tests. Every arrow on this diagram is a protocol that had to be designed and debugged. Everything below is in production today.
Why one engineer and AI
AI accelerates implementation. Experience drives architecture, trade-offs and delivery.
In a small team a large share of the time goes not into building but into handing context between frontend, backend, mobile, DevOps, QA and product. When one engineer owns both the architecture and the implementation, most of those boundaries disappear. AI then cuts the cost of the routine parts on top of that — boilerplate, documentation, tests, research and iterations.
This does not make one person a team in every situation. It means that for products of this class — a few applications, a backend, infrastructure and payments — the work a small team used to do can be delivered by one experienced engineer with AI: fewer hand-offs, less coordination, and a shorter path from an idea to a running system.
How the work goes
Idea → Scope → Architecture → MVP → Production
The service is not "writing code". You can start from a rough business idea; the steps below turn it into a technical product.
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Idea
You describe the problem. A technical specification is not required.
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Scope
We agree on what the first version must do — and what it deliberately won't.
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Architecture
Components, data, integrations and the deployment shape, written down before the code.
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MVP
A working system with the core path end to end, built in production-grade shape rather than as a throwaway prototype.
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Production
Infrastructure, monitoring, releases, payments — and the documentation to keep it running.
Who
Aleksandr Koshcheev
Twelve-plus years in software engineering: a native Android background, production applications used at scale, backend and cloud architecture, offline-first and distributed systems, CI/CD and production releases. AIPT is my own product: architecture, code, infrastructure, operations and billing in one pair of hands.
Have an idea?
Tell me what your business needs
It does not have to be a technical specification — a description of the problem is enough to start. I reply personally.
For collaboration inquiries: dev@ai-powered-testing.com