Most organizations experimenting with AI are still operating in an ad hoc model where individual teams adopt disconnected tools, workflows, and prompting strategies. While this can improve localized productivity, it rarely creates a scalable engineering operating model capable of delivering consistent, governed, and repeatable outcomes across the software delivery lifecycle.
CleanSlate Technology Group approached AI adoption differently: as a platform engineering and delivery governance initiative designed to operationalize AI-assisted software delivery across architecture, development, testing, project delivery, and technical documentation workflows.
The Problem: Ten Engineers, Ten Different Tools
CleanSlate Technology Group, an AWS Premier Tier Partner based in Indianapolis, surveyed their engineering team and found a fragmented AI engineering ecosystem: out of ten engineers, all ten were using different AI platforms; ChatGPT, Copilot, Claude, and more.
The result was predictable: inconsistent delivery of artifacts, limited governance controls, and no scalable operating model for repeatable engineering outcomes. There was no centralized operating model for governing AI-assisted engineering practices.
CleanSlate needed three things:
- A standardized AI engineering platform across delivery teams
- Governance controls including policy enforcement, validation workflows, architectural standards, and operational safeguards
- Reusable engineering accelerators, architectural patterns, and spec-driven delivery frameworks
The goal wasn’t just to find a better tool. It was to establish a governed AI-enabled delivery operating model.
Enter Kiro: Beyond AI-Assisted Development
As an AWS Premier Tier Partner, CleanSlate turned to Kiro, a spec-driven engineering platform designed to orchestrate requirements for generation, architecture design, implementation workflows, and AI-assisted software delivery. Built and managed by AWS, Kiro enables engineering teams to standardize AI-assisted development through reusable specifications and agentic orchestration patterns. Kiro’s spec-driven development approach transforms structured requirements into architecture artifacts, implementation of workflows, validation patterns, and deployable code through agentic orchestration.
But what attracted CleanSlate wasn’t just the coding capability. It was the alignment with their broader mission.
“Kiro allows you to synthesize documents, generate reports, build presentations, combine transcripts, notes, and structured data into delivery artifacts. It becomes an operational platform for how engineering teams deliver work, not just how they generate code.”
— Stephen Henderson, Technical Manager, CleanSlate
That distinction matters. Kiro isn’t a productivity shortcut. For CleanSlate, it became the backbone for how they generate architecture, reports, and client deliverables across the entire business, not just engineering.
“We’ve built a governed operating model for AI-assisted delivery that spans development, project management, assessments, UI/UX, and testing. This extends beyond writing code into the entire software delivery lifecycle.”
— Darren Mills, Chief Technologist, CleanSlate
How They Did It: Framework First, Tool Second
“Other companies hand their teams a tool and say ‘go use it.’ We took the opposite approach: define the operating model first, then embed the tooling into governed workflows with standardized delivery patterns.” — Mills
They started with a small core group of 8–10 people across a few projects. Before anyone opened Kiro for real work, the team focused on building the foundation: reusable steering frameworks, domain-specific skill libraries, engineering standards, and validation workflows that established governance boundaries and expected delivery outcomes.
The Results: When the Framework Clicks
Once the foundation was in place, the results came quickly.
Work that previously suffered from inconsistent outputs, hallucinations, and constant validation loops was replaced by consistent, validated engineering outcomes with significantly reduced rework and validation overhead. The team began catching flaws that were previously hard to find.
Henderson described a concrete example after a client meeting: “I was able to generate architecture artifacts, produce technical diagrams, and assemble executive-ready client deliverables, all within about an hour. But it’s not magic. You need the right inputs, structure, and framework behind it.”
That caveat is important. The productivity gains aren’t accidental. The acceleration came from the governance framework established before platform adoption, not from the AI tooling alone.
The Bigger Shift: Changing What Engineers Do
The gains go beyond speed. CleanSlate’s engineers are changing how they work, not just how fast.
At the leadership level, Mills sees this as a fundamental shift in the development lifecycle: “We’re moving toward writing technical specifications, letting AI generate code, then testing and validating outputs. That fundamentally changes how we structure software delivery.”
This isn’t just how CleanSlate works internally; it’s the delivery model behind every client engagement. When CleanSlate delivers an Application Modernization assessment or a Well-Architected review, the same governed AI workflows, reusable accelerators, and validation frameworks power the work. Clients benefit from a delivery partner that doesn’t just advise on AI adoption; they operate an AI-enabled delivery model and bring clients into it.
Lessons for Organizations Considering AI Adoption
CleanSlate has been candid about what they’ve learned and eager to share it.
Adoption matters more than the tool.
- Prioritize team readiness and willingness to adapt before rolling out any AI platform
- Unstructured adoption introduces delivery inconsistency, governance gaps, and operational risk
- Value comes from consistency, structure, and shared practices, not the tool itself
Build the framework before you build anything else.
- Understand how the tool works and how your team wants to use it before anyone opens it
- Define steering files, internal standards, and output expectations upfront
- A strong foundation is what makes everything downstream reliable
The limit isn’t the tool, it’s how you think about using it.
- Structured inputs and well-designed workflows unlock the full range of what AI can generate
- Architecture, reports, plans, and full workflows are all possible, but only with intentional guidance
- The ceiling rises as your team gets better at directing the AI, not just using it
CleanSlate established governance standards around prompt management, reusable delivery artifacts, human review gates, and validation workflows, ensuring consistency, traceability, and alignment with enterprise delivery and security requirements.
Hear It Directly from the Team
CleanSlate Technical Manager Stephen Henderson will be speaking at the Indy AWS Meet-Up at CleanSlate’s corporate headquarters on April 28th, 2026. Henderson will share how the team built their Kiro framework, what surprised them, and what’s coming next, followed by a live Q&A.
Location: 645 W. Carmel Drive, Suite 140, Carmel, Indiana 46032
Date: April 28, 2026
Registration is open: https://www.meetup.com/indyaws/events/314430843/
See how this works in practice, ask the people who built it, and learn how CleanSlate can help your organization move from AI experimentation to operationalized delivery.