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.
The problem isn't the tools. It's the process around them — how work gets structured before it reaches AI, how decisions get reviewed, how teams stay aligned across the full lifecycle. That process never changed when the tools did.
AIDLC Workspace — SimplyGoose's implementation of the AI-Driven Development Lifecycle (AIDLC) — installs that process. End to end. Across every role, every phase, every team.
The Gap
AI tools made individual contributors faster. They didn’t make the process faster.
When your team adopted AI, something predictable happened. Engineers got faster — sometimes dramatically. A task that took a day takes an hour. Tickets that required three engineers can be handled by one.
But the process around those engineers didn't change. Requirements still come in vague. Context still evaporates between Product and the engineer. Engineering Leads still can't see what's moving and what's stuck without pulling people into a standup. Design decisions still get made implicitly during the build instead of explicitly before it.
That gap — between individual AI velocity and org-level delivery coherence — is where your AI investment disappears. It's not a tooling problem. It's a process problem. And most teams haven't named it yet.
Why Existing Approaches Don't Fix It
Two failure modes. Neither one solves it.
What most teams are doing
Faster silos. Same fragmented process.
Individual engineers use AI inside their own workflow — code generation, autocomplete, local chat. They move faster in isolation. But the handoffs between roles don't change. Requirements are still ambiguous. Reviews are still bottlenecked. Alignment still depends on meetings. The team ships faster fragments, not faster outcomes.
What vendors keep promising
Builds fast. Builds the wrong thing.
AI-autonomous tools promise to turn prompts into products. They generate code quickly — but without structured requirements, architectural guardrails, or human review at the right checkpoints, you get output that looks like progress but doesn't hold up. Rework compounds. Trust erodes. The team reverts to manual oversight anyway.
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 the individual contributors. That's the AI-Driven Development Lifecycle — AIDLC. And AIDLC Workspace is how your team runs it.
AIDLC Workspace
The process your AI tools were always missing.
AIDLC Workspace isn't a PM tool. It's not a code generator. It's the structured process layer that sits across your existing tools and enforces a core mechanic: human decides, AI executes, human reviews — repeated at every phase of delivery.
Inception
Product defines outcomes. AI drafts structured requirements. Humans review, refine, and approve before any code is written. Every ticket enters Construction with full context — acceptance criteria, architecture notes, and role-specific instructions.
Construction
AI builds against the structured spec. Humans review at defined checkpoints — not after the fact. Code review, testing, and QA are embedded in the flow, not bolted on. Every decision is explicit, traceable, and reversible.
Operations
Deployment, monitoring, and feedback loops close the cycle. Issues discovered in production feed back into Inception with full context. The process is continuous, not linear — and every iteration gets faster because the structure compounds.
In Practice
Five UAT bugs.
One session. No coordination overhead.
Here's what AIDLC looks like running on real work. A team found five UAT bugs at the end of a sprint — the kind that normally trigger a multi-day scramble across Product, Engineering, and QA.
- 1
Bugs documented in Inception format
Each bug was captured with full context — reproduction steps, expected behavior, affected components, and acceptance criteria for the fix. AI drafted the initial write-ups; humans reviewed and approved.
- 2
AI built the fixes against structured specs
Because every bug had a complete spec, AI could generate targeted fixes without ambiguity. No back-and-forth. No "what did you mean by this?" conversations.
- 3
Human review at each checkpoint
Every fix was reviewed before merge — not rubber-stamped, actually reviewed. The structured format made review fast because reviewers knew exactly what to check.
- 4
All five shipped in a single session
What would have been a week of coordination debt was handled in one working session. No standups. No status pings. No context switching.
That's not an AI demo. That's a Tuesday — when the process is installed.
What Teams Experience
When the process is installed, engineering stops being the constraint.
"We stopped arguing about how to use AI. The process just tells you. Every role knows what they're responsible for, what AI handles, and what needs human review. The arguments disappeared and the output got better."
Hours, not days
Tasks that used to take days of coordination now resolve in focused sessions. The process eliminates the overhead, not the oversight.
10-15x
Throughput increase on structured work. Not because people work harder — because the process removes ambiguity before AI touches the task.
Every decision
Traceable, explicit, and reversible. No more "who decided this?" or "when did this change?" — the process captures it all by design.
How Teams Get Started
Two ways to implement AIDLC. One right answer for your team.
Every team starts somewhere different. Some need to see AIDLC in action before committing. Others are ready to go and need implementation support. We meet you where you are.
Starting point
AIDLC Workshop
$15K–$25K
Two-day engagement with your leadership and senior ICs. We run AIDLC on your actual work — a real feature, real bugs, real team dynamics — so you can see the process in action before you commit to a full implementation.
Learn moreRecommended
90-Day Implementation
$75K–$150K
Full AIDLC installation across your team. Three arcs — setup, guided execution, and independent operation — designed so the process sticks after we leave. Includes Workspace deployment, role-specific training, and weekly calibration.
Learn moreFor practitioners
AIDLC Certification
$3K–$5K/person
One-day intensive for engineers, PMs, and leads who want to master AIDLC at the practitioner level. Hands-on exercises on real work, certification exam, and ongoing access to the AIDLC practitioner community.
Learn moreShip AI — The Newsletter
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One email per week. Real AIDLC patterns, implementation stories, and lessons from teams actually running the process. No fluff. No pitch decks. Just the stuff that makes your team faster.
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See AIDLC Workspace running
on a real team's work.
We'll walk through your current delivery process, show you where AIDLC installs, and run a live example on your actual work. No slides. No sandbox. Just the process, running on your problems.