A battle-tested, governance-first AI Development Lifecycle that ensures every AI project is delivered on time, on budget, with full traceability and enterprise-grade quality. Built from real-world AI engineering experience.
Traditional SDLC wasn't designed for AI. ADLC is - covering the probabilistic, iterative, and governance-heavy nature of modern AI engineering.
AI systems fail in production not because of poor code, but because of inadequate specification, insufficient evaluation, and absent governance. ADLC addresses all three with a structured, repeatable process that enterprise teams can adopt and audit.
Every ADLC engagement produces a full audit trail - from initial requirements through to production deployment and ongoing monitoring - giving your legal, compliance, and executive teams the visibility they need.
AI readiness assessment, stakeholder interviews, use case prioritisation matrix, data availability audit, and ROI modelling. Deliverable: Discovery Report.
SPARC-driven requirements specification - Situation, Problem, Action, Result, Constraints. Defines what success looks like before a line of code is written. Deliverable: SPARC Spec Document.
Agent architecture design, model selection rationale, safety constraint mapping, data pipeline design, and integration architecture. Deliverable: Architecture Decision Record.
Iterative AI engineering with continuous evaluation loops, red-teaming, prompt engineering, tool integration, and automated testing. Deliverable: Working AI System + Test Suite.
Systematic benchmarking against spec, bias and fairness testing, adversarial robustness checks, human evaluation sessions, and performance profiling. Deliverable: Evaluation Report.
Production release strategy, rollout plan (canary/blue-green), monitoring infrastructure setup, alerting configuration, and stakeholder sign-off checklist. Deliverable: Production System.
Continuous model monitoring for drift and degradation, periodic re-evaluation, compliance reporting, model card maintenance, and incident response procedures. This phase runs indefinitely post-deployment.
Book a discovery call to see how ADLC can transform your AI engineering process.
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Only 1% of organisations have reached full AI maturity. The evidence shows that structured methodology — not more tools — is the differentiator.
| ADLC Phase | Industry Failure Rate (no methodology) | Common Root Cause | ADLC Mitigation |
|---|---|---|---|
| Discovery | 41% — wrong use case | No AI readiness audit or opportunity scoring | Structured capability + opportunity assessment |
| Specification | 35% — scope creep | No formal requirements or success criteria | SPARC-driven specification with measurable KPIs |
| Design | 28% — safety gaps | No safety constraint mapping pre-build | Agent architecture review + red-team design |
| Development | 22% — data issues | Insufficient data quality governance | Continuous eval loops + data contracts |
| Evaluation | 18% — not tested | Skipped benchmarking, no bias testing | Formal evaluation framework + audit trail |
| Deployment | 14% — poor adoption | No change management or user onboarding | Stakeholder sign-off + deployment runbook |
| Governance | Ongoing risk | No drift detection or compliance monitoring | Continuous monitoring + EU AI Act alignment |