SimplyGoose

Your AI tools
are working.
Your delivery
process isn't.

You've invested in Copilot, Claude, and AI licenses across the org. Individual contributors are faster than they've ever been. And your team and org delivery outcomes haven't moved.

It still feels messy. Incoherent. Expensive.

It's not the tools. It's the process. AIDLC Workspace — SimplyGoose's implementation of the AI-Driven Software Development Lifecycle — installs that process.

Three phases. Every approval in git. From intent to production.

AIDLC Workspace — analytics-dashboard
01
Phase 1 · Inception
Intent → Requirements → Approved PR
Intent statement
"Add a usage analytics dashboard so Engineering Lead can see cycle time by squad without a standup."
requirements.mdPR #204 merged
Analytics Dashboard — approved requirements
3 clarifying questions answered · acceptance criteria locked · design approved · 3 units defined
02
Phase 2 · Construction
Units → Context loaded → Code suggested → Reviewed
PR #207-a
Data ingestion layer
contextcode genreviewed ✓
PR #207-b
Metrics API endpoints
contextcode genreviewed ✓
PR #207-c
Dashboard UI
contextcode genin review
PR #207 · parent2/3 merged
Analytics Dashboard — construction
Each sub-PR: full context pre-loaded · AI-suggested code changes · human-approved before merge
03
Phase 3 · Operations
Deployment plan → Approved → Live
deployment-plan.mdLive
Analytics Dashboard — deployed
Rollback plan generated · smoke tests passed · every decision logged in git
10–15×
team throughput vs. unstructured AI
1 session
from intent to first agent-ready unit
3 phases
Inception through Operations, enforced
0 slides
Demo runs on your actual work

The Gap

AI tools are running everywhere. Nothing is connected.

AI tools made individual contributors faster. They didn't make the process faster.

Your team already has AI agents. Copilot in the IDE. Claude in the terminal. AI-assisted tickets in Jira. Each one is making that role faster in isolation.

But there's no shared thread. Product writes a ticket — the engineer's agent never sees the full intent behind it. The engineer ships a PR — the Engineering Lead doesn't know what decision was made or why. AI is running at every step, but each step is its own island.

The problem isn't that AI is missing. It's that the lifecycle has no connective tissue. AIDLC is that connective tissue — and AIDLC Workspace is how it installs.

Without AIDLC
PM
Product
Jira / Linear
Intent lost in ticket format
×context drops
AI
Agent
Claude Code / Copilot
No shared requirement context
×decision lost
Eng
Engineer
IDE + PR
Interprets, fills gaps manually
×no visibility
EL
Eng. Lead
Standup required
No audit trail across any step
Individual
Fast
Org delivery
Flat
With AIDLC
PM
Product
Intent → requirements.md
Approved PR · full context
context flows
AI
Agent
Claude Code / Kiro / Copilot
Reads approved requirements
PR with context
Eng
Engineer
Reviews sub-PRs
Code + context + tests ready
logged in git
EL
Eng. Lead
AIDLC Workspace dashboard
Full audit trail · no standup
Individual
Fast
Org delivery
Moves with the team

Why Existing Approaches Don't Fix It

Two failure modes. Neither one solves it.

What most teams are doing

Faster silos. Same fragmented process.

AI writes the function. AI fixes the bug. Each task is faster in isolation. But nothing connects. Context evaporates between roles. The process stays fragmented. Faster fragments, same incoherent outcome.

What vendors keep promising

Builds fast. Builds the wrong thing.

AI decides. AI plans. AI builds. The demo looks impressive. In production: technically correct, strategically wrong. Accountability is murky. Nobody can explain why a decision was made. Hard to audit. Harder to fix.

There's a third path.

AI executes. Humans decide. At every step, by design. With a structure that makes the whole team faster — not just individual contributors.

That's AIDLC. And AIDLC Workspace is how your team runs it.

AIDLC Workspace

The process your AI tools
were always missing.

AIDLC Workspace is the end-to-end implementation of AIDLC — the structured methodology that turns individual AI velocity into coherent, measurable team delivery.

It's not a project management tool. It doesn't replace Claude Code, Kiro, or Copilot — it's the structure around them that makes those tools work at team scale.

The core mechanic repeats at every phase: AI proposes → Human approves → AI executes → Human approves → next phase. Every decision logged in git, permanently.

Schedule a Demo
Phase 01 · Inception

Requirements → Design → Unit Breakdown

Product writes intent. AI asks clarifying questions and structures the requirement. Tech Lead proposes and approves design. Work breaks into independently buildable units — each with full context pre-loaded. Nothing moves until the requirements PR is merged.

Phase 02 · Construction

Context loaded → Code generated → Reviewed → Merged

Engineers claim units with context already there. AI proposes functional design and generates code. Engineer reviews and approves at every checkpoint. Each unit ships as a sub-PR with suggested code changes, tests, and full context — ready to review, not interpret.

Phase 03 · Operations

Deploy → Monitor → Respond → Every decision in git

AI generates the deployment plan and rollback procedures before anything ships. Engineering Lead approves. AI coordinates deployment and runs smoke tests. Every incident response is AI-proposed, human-approved, and logged.

In Practice

Five UAT bugs.
One session. No coordination overhead.

1
"Create a work item for each bug."

Five structured requirements files, generated. Each one formatted, scoped, and agent-ready. The engineer receiving it knows exactly what done looks like before they open their IDE.

2
Open one. Refine the acceptance criteria. Save.

Structured and agent-ready. Context locked. No ambiguity to resolve mid-build.

3
"Push the Pull Request."

Sub-PRs created per unit. Relevant context pre-loaded. Suggested code changes surfaced for engineer review — not generated and deployed. Surfaced and reviewed. Every approval is in git.

4
Engineer reviews, approves, merges.

Tracked. Auditable. Engineering Lead sees all five — phase, status, who approved — without a standup.

Product isn't juggling four tools. The engineer isn't interpreting a vague ticket. The AI isn't guessing at intent. Engineering Leads can see all of it — without a standup.

What Teams Experience

When the process is installed, engineering stops being the constraint.

We're moving so fast with AIDLC Workspace that Product has become the bottleneck. They can't come up with new features or products fast enough to keep up with how quickly we can build them.

— Engineering Lead, Series B SaaS company

Hours
Not days
Intent to approved requirements, with full context loaded for construction
10–15×
Team throughput improvement
Reported by teams running AIDLC vs. unstructured AI workflows
Every decision
Logged in git
Permanently traceable across every role, every phase

How Teams Get Started

Two ways to implement AIDLC. One right answer for your team.

Entry Point

AIDLC Workshop

$15K–$25K

Range shown — talk to us for exact scoping.

Two days, your team, your in-flight work. We run all three AIDLC phases using projects you're already building. No synthetic exercises. You leave with a customized implementation roadmap and a team that has actually run the process.

Learn more
Most Popular

Primary Engagement

90-Day Implementation

$75K–$150K

Range shown — talk to us for exact scoping.

We embed with your team and implement AIDLC across all three phases on a real AI initiative — not a sandbox. At 90 days: process installed, first milestone shipped, AIDLC Workspace deployed and licensed.

Learn more

For Individual Practitioners

AIDLC Certification

$3K–$5K

Per person. Talk to us for group rates.

One full day, guided. Product track and SDE track. Work through all three phases of AIDLC, learn how to prepare your team and tooling, and leave with the skills to implement it the following week.

Reserve my seat

Ship AI — The Newsletter

Practical AIDLC, every week.

What's working, what isn't, and why — from teams actually running AIDLC. No fluff. No thought leadership. Just the mechanics.

Free. Unsubscribe any time. We don't sell your email.

Ready?

See AIDLC Workspace running
on a real team's work.

Book a 30-minute demo. We'll walk through all three phases on an actual AIDLC repo — from intent statement through deployed PR. No slide deck. No sanitized sandbox. The process, on real work.

Questions? sales@simplygoose.com