AI-augmented
development
Our proprietary methodology where AI generates code and humans take responsibility for the result. Faster delivery, fewer bugs, lower technical debt.
AI generates
code
Humans own
the result
Manifest
9 theses
AI-augmented
not vibecoding
What is
AI-augmented development
AI-augmented development is a software development methodology where AI tools generate most of the code and the developer serves as curator, auditor, and architect. Not vibecoding — a disciplined approach with governance and accountability.
What it means
in practice
AI generates, humans decide
AI tools write code, generate tests, propose architecture. The developer evaluates, adjusts, and takes responsibility for the outcome.
Curator, not operator
The developer doesn't blindly accept AI output. Every suggestion goes through review, testing, and architectural validation.
Development
Code generation, test writing, refactoring. AI accelerates, developers decide.
Code review
AI as first review layer — finding bugs, security issues, inconsistencies. Humans have the final say.
Why not
vibecoding
Vibecoding is prototyping with AI without rules — fast, creative, but unpredictable. Fine for POCs and experiments. But for enterprise software with ISO 27001 requirements, you need governance.
Defined rules
When to use AI, when not to. What inputs, what checks, what metrics.
Audit trail
Who approved what, why, based on what data. Auditable, reproducible.
Security first
AI has no access to production data. Outputs go through security checks. No shortcuts.
Accountability
Humans take responsibility for the result, not the model. AI is a tool, not an author.
Where we use AI
across the project
Analysis & discovery
Processing requirements, analyzing existing code, identifying risks and technical debt.
Architecture
Data model proposals, API contracts, integration scenarios. Humans validate and decide.
Development
Code generation, test writing, refactoring. AI accelerates delivery, developers ensure quality.
Code review
AI as first review layer, finding bugs, security issues, inconsistencies. Humans have the final say.
Documentation
Automatic generation of technical documentation, API specs, changelogs. Humans review and approve.
Testing
Generating test scenarios, edge cases, regression tests. Coverage grows faster, quality improves systematically.
What it means
for you
AI-augmented development isn't about replacing developers. It's about making them more effective at what matters — architecture, business logic, and accountability.
Faster delivery
Less routine work means more time for what requires human judgment — architecture, security, business logic.
Higher quality
More tests, more consistent code, less technical debt. AI doesn't come late, doesn't forget, doesn't cut corners.
Lower risk
Governance and security checks are part of the process, not an add-on. ISO 27001 applies to everything, including AI.
Transparency
You always know what AI generated and what a human approved. Full audit trail, reproducible decisions.
Frequently asked questions
Yes. Every AI output goes through human review, security checks, and automated tests. ISO 27001 governance applies to AI usage too.
No. AI handles routine tasks. Developers focus on what matters — architecture, business logic, security, accountability.
Depends on the project. Typically 30-50% faster for routine development tasks. Architecture and design time stays the same — that's where human judgment is irreplaceable.
