AIDLC Workspace
The process your AI tools
have been missing.
You invested in Copilot, Cursor, and Claude. Your team still ships the same way it did before. The bottleneck was never the AI — it was the absence of a process designed for AI. AIDLC (AI-Driven Development Lifecycle) Workspace installs that process end to end: onboard your codebase, implement the lifecycle, run every sprint through it, and track the results — all in one place.
Why AI Adoption Stalls
The tools got faster. The process stayed the same.
The AI-Assisted Trap
Developers use AI to autocomplete code inside the same workflow they already had. The result: faster silos. Each engineer gets a personal speed boost, but the team never changes how it plans, reviews, or ships. You get faster typing, not faster delivery.
The AI-Autonomous Trap
Teams point an agent at a ticket and let it run. The result: it builds the wrong thing — confidently. Without structured requirements, approval gates, and human checkpoints, autonomous AI creates more rework than it saves.
AIDLC is the third path. A structured process where AI proposes and executes while humans define intent and approve every decision. Not assisted. Not autonomous. Collaborative by design.
End to End
Onboard. Implement. Run. Track. The full lifecycle, in one place.
Onboard
Connect your repos, import your docs, and let the Workspace build a structured knowledge layer — org standards, team conventions, system architecture — so every AI interaction starts with the right context.
Implement
Walk your team through the AIDLC phases — Inception, Construction, Operations — with guided workflows, templates, and approval gates baked in. No guessing what comes next.
Run
Execute real sprints through the lifecycle. AI proposes plans, asks clarifying questions, and executes approved work. Humans review, approve, and validate at every step.
Track
Measure what matters: cycle time, approval throughput, rework rate, AI contribution ratio. See whether the process is working — and where to tune it.
The Lifecycle
Three phases. Every role. AI-native from first requirement to production.
Phase 1
Inception
Hours to days
Turn a business goal into structured requirements, a design document, and decomposed implementation units — all AI-generated, all human-approved. The sprint starts with shared clarity, not a vague ticket.
Phase 2
Construction
Days to weeks
AI picks up each unit, proposes an implementation plan, asks clarifying questions, then writes the code. Engineers review every plan and every output. Nothing merges without human approval.
Phase 3
Operations
Ongoing
AI generates deployment plans, runbooks, and monitoring configurations. The team reviews, approves, and ships. Post-deployment, the Workspace tracks incidents and feeds lessons back into the knowledge layer.
How It Works
The same pattern. Every phase. AI proposes. Humans decide.
AI proposes a plan
Based on structured context and requirements, the AI generates a concrete, reviewable plan for the next piece of work.
AI asks clarifying questions
If the AI detects ambiguity, missing context, or conflicting requirements, it pauses and asks — instead of guessing.
Human approves
A human reviews the plan, answers questions, and gives explicit approval before any execution begins.
AI executes. Human validates.
The AI carries out the approved plan. The human reviews the output, requests changes if needed, and signs off.
This loop repeats at every phase — Inception, Construction, and Operations. The result: every AI-generated artifact has a clear audit trail, a human decision behind it, and a structured path to production.
Under the Hood
Markdown and git. Not our database.
Every artifact the Workspace produces — requirements, design docs, implementation plans, audit logs — is a Markdown file in your repository. Every approval is a commit. Every decision is traceable in your git history. We never lock your process into a proprietary database. If you stop using the Workspace tomorrow, your entire process history stays in your repo.
“The approval is the merge. The audit trail is the git log. The sprint board is the repo.”
Deployment Options
Hosted or self-hosted. Running this week either way.
SimplyGoose Cloud
$49 – $79 / seat / mo
Sign up, connect your repos, and start running the lifecycle today. We handle hosting, updates, and infrastructure. Your artifacts still live in your git repos — we just provide the coordination layer on top.
Enterprise Self-Hosted
Annual license
Run the Workspace inside your own infrastructure. Same product, your network. Includes dedicated onboarding, priority support, and custom integrations with your existing toolchain.
What Running It Looks Like
When AIDLC is working, engineering stops being the bottleneck.
“We went from two-week sprints with half the tickets done to shipping every planned unit in a week. The process didn’t slow us down — it removed the ambiguity that was slowing us down.”
60%
reduction in cycle time
3x
more units shipped per sprint
< 5%
rework rate on AI-generated code
Is This Right for Your Team?
Three types of teams AIDLC Workspace isn't for.
Teams not yet using AI in development.
If your team hasn't started experimenting with AI coding tools, you need adoption before you need a lifecycle. We're happy to chat, but the Workspace isn't your first step.
Solo developers.
AIDLC shines on teams where coordination is the bottleneck. If you're a solo dev, the overhead of structured phases and approval gates will slow you down, not speed you up.
Teams still deciding whether AI belongs in their workflow.
The Workspace assumes the decision is made. If you're still evaluating whether AI-assisted development is right for your org, start with a pilot — not a process overhaul.
If none of those fit — let’s talk.
See AIDLC Workspace on a real team’s work.
We’ll walk through the lifecycle on your codebase, with your requirements, so you can see exactly what running AIDLC looks like for your team.