AI in software development
AI needs an experienced driver – we help you separate real value from hype and build solutions that pay for themselves.
AI churns out code in seconds and plugs into existing systems without much effort. That makes it easy to believe everything is about to speed up.* In reality, much of the time goes into fixing the generated code and clearing review bottlenecks. The real value of software development still comes from elsewhere: architecture, quality assurance and knowing where AI is worth using – and where it isn’t.
*In Google’s DORA research, around 80% of developers felt AI had improved their productivity. When METR measured the work of experienced developers, the result was different: tasks took 19% longer with AI, even though the developers themselves estimated they had been 20% faster.
What does AI mean in software development?
AI as part of real development work
In our experience, writing code takes up only about 30% of a developer’s working time. AI speeds up that share, but the bottlenecks are elsewhere: in architecture, quality and understanding the business logic. That’s where you need an experienced developer.
Faster learning and validation
AI enables faster experimentation and iteration. Ideas can be validated early, without heavy investment or long development cycles.
Keeping technical debt in check
Uncontrolled AI-generated code piles up technical debt and clogs code reviews. Used right, AI can also reduce that debt: it can analyse large codebases, improve test coverage and support controlled refactoring. The prerequisite is that every change fits the overall architecture, so the system stays maintainable down the line.
Backing up expertise
AI is a developer’s pair programmer, not a replacement. It helps define the problem, map out the options and evaluate solutions. The more code is machine-generated, the more senior-level experience matters. You need people who can challenge AI’s answers, understand the business constraints and take responsibility for the outcome.
What we do?
We go through your product and development process together before a single line of code is written. We look for the spots where AI speeds up development or gives your product a competitive edge. Finding the spots where it doesn’t help is just as important. The cheapest place to catch a wrong turn is on paper.
We also help teams or entire organisations adopt AI: choosing the tools, agreeing on the ground rules and showing in practice how it all works.
AI features built into products
An AI solution that works in a demo doesn’t automatically work in production. We build AI features into business-critical systems: API integrations, retrieval-augmented generation (RAG), decision support and agent-based workflows. We also put your organisation’s existing data to use, right down to the unstructured odds and ends. We make sure data moves securely and that every solution can be tested and monitored.
How we do it?
Using AI in software development – thinking first
Our developers use Claude Code, Cursor and GitHub Copilot every day. But the tool doesn’t decide what gets built. We use MCP integrations, semantic search and vector databases when they fit the client’s technical setup.
Critical assessment and quality assurance
Language models aren’t infallible oracles. That’s why generated code and the output of agent chains are always reviewed. Generation, automated testing and human review are kept separate, so each step checks the one before it. AI can draft, but an experienced developer is always responsible for quality, security and production readiness.
Frequently asked questions about AI
Get in touch – let’s go through your product ideas, risks and implementation path together and turn them into an AI roadmap that fits your business. 👇
