AIDLC.
SDLC got software to production. AIDLC gets AI there.
Overview
Why AIDLC exists.
Software engineering solved delivery with SDLC: version control, CI/CD, staging, review gates. AI broke that playbook — models are probabilistic, data drifts, and 'works in the demo' means nothing.
AIDLC is the lifecycle we developed internally to ship every Aurelia engagement: requirements expressed as eval suites, data and model versioning as first-class citizens, golden-set regression on every change, shadow deployments before traffic, and drift operations after go-live.
We use it on every project. Clients adopt it to make their own AI teams ship with the same discipline.
Core capabilities
What it does.
Eval-first requirements
Success criteria become executable eval suites before any model is trained.
Data & model versioning
Every artifact traceable: which data, which weights, which prompt, which result.
Progressive deployment
Shadow runs and A/B gates before any model takes production traffic.
Drift operations
Monitoring, retraining triggers, and incident playbooks for the life of the system.
Next in the line