AI_Flow integrates AI into software engineering workflows by implementing a layer of control, context and validation. Every interaction is organized within a structured process aligned with system architecture, turning AI into a predictable collaborator.
AI_Flow is based on AECF (AI Engineering Controlled Framework), a governance framework that guides the use of AI in software development, ensuring every iteration remains aligned with architecture, technical constraints and project goals.
The challenge is governing AI-generated development
Five governed phases that transform unstructured AI prompting into a deterministic, auditable engineering workflow.
Define the objective and generate a structured task specification aligned with project architecture.
Validate architectural decisions before any implementation begins. Gate: GO required.
AI-assisted development preserving project context, technical constraints, and coding standards.
Analyse generated code to detect inconsistencies, quality issues, security risks, and regressions.
Register changes as auditable artifacts, ensuring full traceability and engineering knowledge preservation.
Real-world AI engineering scenarios where AECF delivers measurable impact.
AI-assisted development with full governance, audit gates, and traceability.
Modernize legacy codebases using AI with architectural context preserved at every step.
Generate accurate, structured technical documentation from existing code with AECF control.
Automated code quality, security, and consistency audits on AI-generated or human code.
Capture and reuse senior engineering decisions as structured AECF artifacts.
Transform informal senior engineering workflows into repeatable, governed processes.
AI collaboration that always knows the system boundaries, constraints, and design decisions.
End-to-end AI coding workflows with deterministic output, gates, and full audit trail.