Turn AI usage into a measurable software delivery system.
ADLC helps engineering organizations design and run repository-native AI-assisted workflows with governance, human control, and measurable delivery metrics built directly into the software development lifecycle.
45-min intro call · no commitment
Built for CTOs, VP Engineering, and delivery leaders who want AI adoption without losing control.
Not a separate platform
Every stage is a declarative file under .claude/workflows/ — versioned with the codebase, reviewed like code, and travelling with the project.
Human-governed by design
AI supports execution, but architecture choices, commits, pull requests, and release decisions remain human-controlled.
Measured delivery flow
Throughput, WIP, cycle time, blocked work, and delivery flow become visible so teams can improve with evidence.
Most engineers still use AI assistants like chat tools instead of integrating AI directly into the development workflow.
In many teams, engineers still copy and paste code into chat windows, ask for fixes, request explanations, or troubleshoot errors manually. AI is often treated as a separate assistant conversation instead of becoming part of the actual engineering workflow inside the repository and delivery process.
ADLC changes the focus from individual AI productivity to the performance of the entire delivery system.
Proven in a real enterprise software project with delivery metrics
ADLC was implemented in real production engineering environments, not a demo. The first full implementation supported a U.S.-based company in the insurance sector across three projects, becoming a repository-native AI-assisted delivery workflow and later evolving into a broader operational model for scaling engineering execution.
Steps: Ticket → Commit
Specialized Agents
Governed Workflows
Throughput Progression (Items/Day)
Baseline throughput before the structured AI-assisted workflow was introduced.
Throughput increased after the workflow became part of daily engineering execution.
Delivery remained high even after part of the team was redeployed, suggesting flow efficiency rather than staffing effect.
Throughput (Delivery Pace)
28-day rolling average of accepted work and completed work from the source dashboard.
A framework for defining governed engineering workflows.
ADLC is not a single hardcoded workflow. It is a framework for defining and evolving repository-native engineering workflows with AI-assisted execution, governance, delivery metrics, and human approval gates.
The workflow shown here is one example: a path from ticket to reviewed and tested code ready for commit.
Assessment & AI readiness
Review repository structure, local execution, test reliability, backlog quality, delivery flow, and engineering constraints.
Workflow definition
Define project-specific workflows, commands, agents, responsibilities, governance gates, tool integrations, and engineering conventions.
Repository-native implementation
Configure the workflow inside the repository so it is versioned, transparent, and aligned with the team's codebase.
Human-in-the-loop execution
human gateDevelopers choose approaches, review changes, approve commits, and keep accountability for what ships.
Metrics and delivery visibility
Track throughput, WIP, cycle time, blocked work, and delivery patterns across sprints.
Optimization and scaling
Refine workflow based on data, expand to additional teams, and introduce cost and quality metrics over time.
Metrics and delivery visibility
Throughput
Track completed work over time and compare delivery before and after ADLC adoption.
Cycle time
Understand whether delivery is becoming more predictable and where delays appear.
WIP stability
Keep flow healthy as implementation speed increases, without creating uncontrolled multitasking.
Next metrics direction
Extend measurement toward cost efficiency, defect trends, rework, test validation, and review consistency.
AI acceleration without uncontrolled automation.
We believe AI should act as a high-throughput capability multiplier, not an autonomous agent running wild in your repository.
ADLC enforces strict system boundaries. Your engineers remain the final arbiters of code quality, architecture direction, and deployment readiness.
Built by teams already trusted in production delivery environments.
ADLC is not created by AI consultants experimenting in isolation. It is built by engineering leaders and delivery teams with experience supporting enterprise software initiatives, senior resource delivery, QA automation, and production project execution.
This matters because AI-assisted SDLC adoption only works when the underlying engineering delivery discipline is strong: clear ownership, reliable communication, senior talent, and predictable execution.
Senior delivery capacity
Rapidly adding experienced engineers and QA specialists into demanding enterprise environments when internal hiring capacity is limited.
Operational trust
Working inside client delivery structures with proactive reporting, transparent communication, and strong ownership of outcomes.
Production discipline
Practical software delivery experience across enterprise platforms, QA automation, Azure/.NET ecosystems, and distributed engineering teams.
Prometheus Group — enterprise engineering support
A separate 9-month enterprise delivery engagement with Prometheus Group demonstrates the team’s ability to provide senior engineering and QA talent, integrate into client teams quickly, and maintain delivery momentum under real operational constraints. This is proof of engineering and delivery maturity behind the ADLC offering.
Supported enterprise asset management software delivery from August 2025 to April 2026.
Embedded senior QA automation and software engineering specialists into active delivery teams.
Supported QA automation, .NET/C#, Xamarin mobile, Playwright, CI/CD, Docker, GitLab, SAP and Oracle Primavera P6 integrations.
Provided CTO-level progress visibility, proactive escalation, and structured coordination across multiple teams.
“Clever Spark proved to be a reliable partner during our engagement. Their engineers onboarded quickly, adapted to our environment, and supported both QA automation and development work effectively.”
Start with a controlled pilot, then scale what works.
Assessment
We map your current delivery pipeline, identify AI readiness, and define the highest-ROI entry point for ADLC integration.
Implementation
We co-build the repository-native workflow with your team, establishing templates, governance rules, and agent orchestration.
Run, Measure, Optimize
Your team runs the workflow. We track throughput metrics and iteratively refine the agents and prompts to maximize flow.
ADLC is not a single AI workflow. It is a system for building AI workflows.
Stop waiting for individual developer productivity to magically translate into software delivery. Engineer your AI lifecycle today.