HOW WE DELIVER · AI-DLC IN PRACTICE
Our AI-led software development lifecycle
We connect requirements, AI-assisted engineering and release through eight defined stages. Your team approves the specification, technical plan and release. Greenlight is our implementation of this approach, keeping decisions and evidence connected to the work.
Give AI the context to build the right software.
Useful software starts with clear requirements, shared context and decisions your team can trace. Our delivery approach addresses four common gaps before they become rework. So we put a discipline around it, the one running through our engineering services. Your assistants still write the code. What we govern is how work travels from an approved requirement to a released feature, and which of those steps a human has to sign.
The story-to-code jump
A loose requirement goes straight to generated code with nothing structured in between.
Inconsistent context
Context is copy-pasted into prompts, incomplete and different every time.
Invented assumptions
Ambiguity gets filled by the model instead of resolved by a person.
Stranded reasoning
The reasoning lives in a chat history nobody reopens, and it leaves when people do.
Our AI-led software development lifecycle.
Constitution
Your team sets the standards and constraints that guide every AI-assisted stage.
Specify
AI drafts behaviour and acceptance criteria. A product owner holds anything that is incomplete.
Clarify
AI routes open questions to the responsible owners, then updates the specification for approval.
Plan
AI proposes the technical approach, tests and rollback. Engineers approve it or send it back.
Map impact
AI traces affected systems and dependencies. New risks return the work to planning.
Tasks
AI turns the approved plan into sequenced, reviewable work for the delivery team.
Build
Agents generate, test and integrate changes. Failed checks return the work for correction.
Review and release
AI assembles the evidence. The release owner approves the change or holds it for review.
Eight stages take an agreed outcome through to release. Each stage names the work AI performs, the evidence it produces and the decision that remains with your team. Approval gates: specification, technical plan and release. Open questions return to clarification before the specification is approved.
Three approvals keep your team in control.
Every gate can say no. That is the whole claim: the hold path is as real as the pass path.
From inception to operation.
We organise the work into three phases so business and engineering teams can see what is being decided, built and handed over.
Ongoing monitoring, incidents, model drift and cost management need an agreed operating scope. We define ownership and the handover to AI managed services or Greenlight Operations before release.
Judge the result by what reaches production.
We agree a baseline with your team and track delivery quality alongside speed. These measures show where the lifecycle needs to improve.
Review latency
How long changes wait for a review or approval. Use this to find queues that faster code generation may expose.
Escaped defects
Issues discovered after release. Use this to assess the effectiveness of tests and approval gates.
Rework rate
The share of delivered work that needs correction. Use this to improve requirements, context and review quality.
Explore why AI coding tools alone do not determine delivery performance.
AI proposes. Checks run. Your team approves.
Proposes
An agent proposes the change for its stage, working from source-linked project truth rather than a pasted prompt.
Check
Automated tests and security checks surface failures, regressions and policy violations for review.
Greenlights
Then a person greenlights it. Either check can block and send the work back, and every decision is logged so it can be replayed.
Inside every stage, one unit of work moves through the same three beats. On the roadmap: an independent digital twin, running on a different model family, that re-verifies every stage against the requirement and the existing code. The human greenlight is what ships today, and it is what we will hold ourselves to in a contract.
Make delivery repeatable and accountable.
Predictability
The same governed steps run on every change, so delivery stops depending on who happens to be at the keyboard.
Control
No direct story-to-code jump. Specification and plan are signed before code begins, and release is signed before anything ships.
Auditability
Specifications, plans, and decisions are linked to the work item. The path a change took is reconstructable, not remembered.
Scale
What worked becomes a reusable engagement template: a process teams adopt, not a habit a few people carry.
Choose the team size that fits your roadmap.
Start with one delivery pod or coordinate several teams under the same standards and approval gates. You retain the roadmap and priorities.
Pilot, measure, standardise, then scale.
Pilot
Run the lifecycle on one real engagement with success metrics agreed up front. An AI Readiness Assessment is the usual starting point.
Measure
Compare against your own baseline, not our benchmark.
Standardise
Codify what worked into a reusable engagement template.
Scale
Extend across teams under central governance.
You do not adopt a lifecycle by announcing it. We configure it to your standards and run it with you on real work, so the evidence arrives before the mandate does. MEASURED ON · CYCLE TIME · ESCAPED DEFECTS · REWORK RATE
The ones engineering leaders ask first.
No. The lifecycle is deliberately tool-agnostic. It governs how work moves from an approved requirement to a released feature, and it sits around whichever assistants your team has standardised on rather than replacing them.
Only the gates that carry risk need a person, and they are the ones you would have reviewed anyway, moved earlier. The specification and the plan are cheap to correct. Generated code that reached production on an invented assumption is not.
The clarify stage exists for exactly that. Ambiguity is surfaced as an open question and resolved by a person before planning, rather than silently filled in by a model that has to produce something.
Specifications, plans, and decisions are linked to the work item, so the path a change took is reconstructable rather than remembered. That is the point of the mapped impact and the logged decisions: the answer to "why was this shipped" exists after the people who shipped it have moved on.
Greenlight connects the specification, plan, work items and approval evidence. Your team keeps responsibility for scope, technical decisions and release. The workflow can use the AI tools your engineers already work with.
Agree a baseline before the pilot, then track cycle time, review latency, escaped defects and rework. Review the results with your team and adjust the workflow before extending it across more teams.
That is the normal case. Where you already have architecture principles, security rules, or a review culture that works, those become the constitution the lifecycle references. We bring the sequence and the gates, not a demand that you discard what already holds.
Start with one engagement. Build evidence before you scale.
Tell us what your team is building and where delivery slows down. We will help define a pilot, the approval gates and the measures of success.