Services / AI Assisted Engineering
Most AI coding pilots stall. We know why.
Not because of the tool — because of the infrastructure around it. We help teams build the feedback loops, practices, and CI/CD foundations that make AI-assisted engineering actually work.
AI coding tools can make your team significantly faster — or produce expensive technical debt. The difference isn't the tool, it's what surrounds it: feedback loops fast enough to keep the model on track, CI/CD pipelines that handle increased code churn, and review processes that retain human judgement while leveraging AI speed.
We've spent years building production software with Claude Code and OpenAI Codex — including Kunnus, our own compliance SaaS. We know the difference between AI-assisted engineering and "AI slop" because we've seen both — and we know which practices make the difference.
Why most AI pilots stall
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CI/CD is too slow — a 20-minute build destroys the feedback loop that AI coding requires.
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No review process for AI-generated code — without human quality gates, you get subtle but expensive technical debt.
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Tooling without context — buying Copilot licences is not the same as building AI-assisted engineering.
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Missing validation loops — without TDD, linting hooks, and automated checks, AI produces confident but incorrect code.
What we offer
AI Engineering Readiness Audit
Before AI tools can help, the foundation has to be right. We assess your CI/CD speed, platform maturity, security scanning, IaC posture, and team workflows.
Deliverable: A prioritised readiness report with concrete tool recommendations for your stack, maturity level, and compliance requirements.
LLM Workflow Workshop
Hands-on with your actual stack, not generic demos. Effective prompting, feedback loop design, code review with AI, agentic coding patterns.
Deliverable: Your team learns pre-commit hooks, LLM hooks, and TDD integration — the foundations that ensure output quality.
AI-Assisted Engineering Embedding
We work directly with your team to establish the practices, tooling, and CI/CD infrastructure that make AI coding tools actually work at scale.
Deliverable: Multi-pass review cycles, quality gates, and platform foundations for organisation-wide AI adoption.
Case study
How we built Kunnus
From a vibe-coded prototype to production SaaS — with AI-assisted engineering and a human in the loop throughout.
Validate fast before you build
We built a vibe-coded prototype in days and shared it immediately with manufacturers. Critical insight: CRA compliance in manufacturing is fundamentally different from software compliance. Many components come from third-party suppliers without source code. Teams are engineering professionals, not software developers.
Without this early feedback, we would have built on false assumptions for months.
Monolith for velocity, human in the loop
Next.js monolith for maximum speed. TDD from day one. Pre-commit hooks running tests, linting, formatter. LLM hooks for automated feedback on every commit. More human steering and refactoring in the beginning — while the model was still building context.
Feedback loops before velocity. The hooks ensured every AI-generated commit passed tests before it landed in the repo.
Rust backend: validate against desired state
Refactored to Next.js frontend and Rust backend for performance and resource efficiency. Initially we didn't have the old backend in the feedback loop to check correctness — so we had to fix more issues. Rust + pedantic Clippy + compiler + TDD now give very high confidence.
Always include the desired end state in the validation loop. Without a reference point, AI generates confident but subtly incorrect code.
Focused review cycles per PR
Every PR goes through multiple Claude review passes — each focused on a specific concern: maintainability, testability, security, performance. Each pass produces concrete refactoring tasks. The human decides and implements.
Focused single-concern passes yield better results than a single comprehensive review. The result: production-grade code that maintains high standards despite AI-assisted origins.
FAQ
What our clients want to know.
How do we start with AI coding tools without creating technical debt?
The key is fast feedback loops from day one: TDD, pre-commit hooks, and automated linting checks catch mistakes before they reach the repository. We help teams establish these foundations before tool adoption, so AI-generated code meets the same quality standards as hand-written code.
What CI/CD prerequisites do we need for AI-assisted engineering?
Your pipeline needs to be fast enough not to break the feedback loop — ideally under 5 minutes for a full build. You also need automated security scans, test coverage gates, and reproducible builds. We assess this in our Readiness Audit and provide concrete recommendations for your stack.
Is AI-generated code GDPR compliant and how do we handle data residency?
It depends on what data gets sent to the AI API. Source code containing personal data or trade secrets requires special care. We help you configure workflows that keep sensitive code local, and advise on EU-compliant hosting options for LLM APIs.
What is the difference between AI-assisted engineering and just buying Copilot?
Copilot is an autocomplete tool — AI-assisted engineering is a way of working. The difference lies in structured review processes, validation loops, and integration with your existing quality assurance. Without these practices, AI tools produce code that compiles but becomes expensive long-term.
How do we measure ROI of AI coding tools in our engineering team?
Forget lines-of-code metrics — they are misleading. We measure cycle time (idea to production), defect escape rate, and developer satisfaction. Teams typically see 30–50% shorter cycle times after 8–12 weeks with established practices, while maintaining or improving code quality.
Ready to build AI engineering the right way?
A free conversation about where you stand and what makes sense next. No hard sell — just honest assessment.