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Proprietary Methodology

The ADLC Methodology

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.

What Is ADLC

AI needs its own Development Lifecycle

Traditional SDLC wasn't designed for AI. ADLC is, covering the probabilistic, iterative, and governance-heavy nature of modern AI engineering.

Why ADLC Exists

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.

✓Full documentation at every phase
✓Governance checkpoints prevent scope creep
✓EU AI Act alignment built in from day one
✓Reproducible, auditable AI decisions
ADLC
AI Development Lifecycle
Governance-First Production-Grade EU AI Act Ready
The 7 Phases

ADLC Phase by Phase

1

Discovery & Scoping

AI readiness assessment, stakeholder interviews, use case prioritisation matrix, data availability audit, and ROI modelling. Deliverable: Discovery Report.

2

Specification

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.

3

Design & Architecture

Agent architecture design, model selection rationale, safety constraint mapping, data pipeline design, and integration architecture. Deliverable: Architecture Decision Record.

4

Development

Iterative AI engineering with continuous evaluation loops, red-teaming, prompt engineering, tool integration, and automated testing. Deliverable: Working AI System + Test Suite.

5

Evaluation & Testing

Systematic benchmarking against spec, bias and fairness testing, adversarial robustness checks, human evaluation sessions, and performance profiling. Deliverable: Evaluation Report.

6

Deployment

Production release strategy, rollout plan (canary/blue-green), monitoring infrastructure setup, alerting configuration, and stakeholder sign-off checklist. Deliverable: Production System.

7

Governance & Monitoring

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.

7
Structured phases
Every stage
Logged and auditable
EU
AI Act aware by design
ISO
42001-aligned controls

Apply ADLC to Your Next AI Project

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