AI Engineering · Accelerated Deployment The SDLC, re-engineered around AI.
AI works as a central collaborator across the software development lifecycle (SDLC) — planning, task decomposition, design and real-time work alongside engineers. Humans stay accountable for validation, decisions and oversight. Velocity, governed.
Why rethink the SDLC
Methods built for humans can’t keep pace with agents.
AI’s capabilities now extend well beyond code generation — into requirements elaboration, planning, task decomposition, design and real-time collaboration with developers.
- 01That shift is driving AI-led orchestration of the development process itself.
- 02Existing software development methods were designed for long-running, human-driven processes — not for AI’s speed, flexibility or agentic capabilities.
- 03Manual workflows and rigid role definitions limit how much value teams can draw from AI.
- 04The reimagined SDLC puts AI at the centre of the workflow — aligning roles, iterations and decisions for faster execution, smooth task hand-offs and continuous adaptability.
How the roles change
AI orchestrates. Humans validate.
Product managers, developers, QA engineers and DevOps engineers retain ultimate responsibility for validation, decision-making and oversight.
AI orchestrates
Planning. Task decomposition. Architectural suggestions. Domain mapping. Documentation. Real-time hand-offs between stages.
Humans validate
Product managers, engineers, QA and DevOps own the gates. They accept, reject and refine. Final accountability stays with the team that ships.
The workflow
A typical accelerated-deployment loop.
Each loop starts with stakeholder input — the business problem, constraints and success criteria, captured in plain language. AI drafts every step and adapts to feedback; a human reviews each one.
/ 01
Extract entities and bounded contexts
Domain design starts from the brief: an LLM parses it into entities, relationships and bounded contexts.
/ 02
Visualise the domain map
Miro AI and eraser.io turn the conceptual map into a diagram, refined visually before the architecture hardens.
/ 03
Generate the logical architecture
An LLM and Structurizr apply the C4 model to define components, containers and relationships — version-controlled and reviewable.
/ 04
Design logical data and APIs
We draft schemas and API contracts from the architecture in dbdiagram and Stoplight, and keep them in sync.
/ 05
Document the workflow
Notion AI and Confluence AI keep living documentation current. Findings inform the next stakeholder brief, and the loop closes.
Candidate tools
The tools we use at each stage.
Domain design
- LLM
- Miro AI
- eraser.io
Architecture
- LLM + Structurizr
- C4 model
Data and API design
- dbdiagram
- Stoplight
Documentation
- Notion AI
- Confluence AI
Where it fits
One of the two pillars of how we ship.
Every tier of our engagement model passes through Accelerated Deployment, alongside Governance & Trust, which enforces the gates AI cannot cross unsupervised. It forms part of the GenAI Delivery Factory SDLC, takes tangible form in the Future of Work coworker model, and is checked for drift by Agent Evaluations.
Next step
We are an intent away.
Bring a stakeholder brief. We’ll walk it through the loop — entities, domain map, architecture, data and API design, documentation — with your team owning every gate.