AI-augmented software engineering
Senior-level delivery at agent speed: one experienced engineer plus AI agents produce what used to take a team — at production quality.
What it means in practice
I use AI agents (Claude Code) in daily development: breaking down specs, implementation, tests, refactoring, and documentation flow through agents, while architecture, review, and production deployment stay in senior hands. The result matches a good team's output — in weeks, not quarters.
This is not an experiment. It's the same setup I run my own production systems with: the Willit marketplace and the Rakentaja Mediat platforms with 300k+ registered users.
Typical engagements
- A new feature or integration in an existing product — from spec to production
- An MVP or product platform built from day one to be extended
- Overdue technical work: test coverage, performance, security fixes
- Stacks: Rails/Hotwire, React/Next.js, Python/Django, AWS, Terraform
Why quality doesn't suffer
Agent-written code is reviewed like any developer's. Tests, lint, and hooks enforce standards before commit, and I put my name on the result. Quality is a property of the process — and the process is exactly what I sell.
FAQ
- What does AI-augmented engineering mean in practice?
- A senior engineer directs agents that do much of the mechanical implementation. The engineer owns architecture and quality — agents multiply the speed.
- Does code quality suffer?
- Not with review, tests, and hooks in place. Quality is a property of the process, not the typist.
- What projects is this a fit for?
- Features, integrations, MVPs, and platforms — work with tight timelines and production-grade quality requirements.
Related services
- Claude Code rollout — if you want your own team doing this
- Legacy modernization — when the target is an aging system
- Fractional CTO — when you need ongoing technical leadership
Tell me what you're building — the first 30-minute call is free.