AI powered SDLC

Our Launch AI SDLC Offering is designed to help organizations accelerate their adoption of AI into their software development lifecycles process and accelerate business outcomes.

A Modernization of the Agile Process Using Microsoft AI Tooling

3-Step Approach:

1. AI SDLC Workshop
2. SDLC Diagnostic
3. Embedded Team Enablement

We go beyond tooling – deep into the process with performance measurement to drive meaningful adoption and success.

Overview

Key Changes to the Traditional SDLC

Human-in-the-loop as Directors and Verifiers:

• Directors feed the AI, generating output (documentation, design mockups, code, etc).
• Verifiers review, provide feedback / refine, and ultimately approve the AI’s work.

Continuous, AI-enabled Test-Driven Development: AI-generated and automated tests, including done criteria required before development

Shorter Delivery Cycles: Embracing agile principles with daily updates, featuring two development waves and a planning day within a 1-week cycle

Value Proposition

CHALLENGE

Most engineering organizations are stuck in “Pilot Purgatory” in applying AI in their software engineering process, unable to fully leverage AI tools. The issue is they are focused on tools and legacy workflows, not people, process, or data.

SOLUTION

The Launch AI SDLC — a transformation of the software delivery lifecycle leveraging Microsoft AI tooling, customized GitHub Copilot Skills, and Launch embedded teams.

KEY BENEFIT

Enable teams to reclaim 40–70% of capacity, improve software quality, track delivery health metrics, and ensure teams are AI capable.

Highlights

• Outcome Driven: Measurable results; clear ROI
• Talent Enablement: Practical, applied upskilling
• Adoption Acceleration: Customized agents and reporting
• Unified Toolchain: Single control plane; zero sprawl
• FinOps & Cost: Integrated forecasting & management

Three Phased Approach

Launch AI SDLC approach provides a structured, end-to-end framework to help organizations accelerate their journey to adopt AI as a central part of their software engineering process — successfully.

1. AI SDLC WORKSHOP

Goal: Showcase the AI SDLC, develop key insights into current org and model

Structure: Remote working session with 3-6 client resources

Timeline: 1-3 days

Activities:

• Prep Call
• Session
• Readout

Deliverables: Readout with key insights and recommendations

2. SDLC DIAGNOSTIC

Goal: Gather insights on last 90 days of digital exhaust to set baseline

Structure: Remote integration with client systems and team using AI Agents in GitHub and Azure DevOps

Timeline: 1-2 Weeks

Activities:

• Prep Call + Planning
• Systems + Agent Integration
• Data Capture + Reporting

Deliverables: Delivery scorecard and recommendations

3. EMBEDDED TEAM ENABLEMENT

Goal: Activate and enable a software team to adopt AI

Structure: Embedded enablement and integrations with AI Agents in GitHub and Azure DevOps

Timeline: 5-26 Weeks

Activities:

• Week 1: Planning
• Week 2: Team set-up + training
• Week 3-5: Enablement, reporting, and agent customization

Deliverables: Customized Agents, Dashboards, and enabled team

Our AI Copilot Agents

• Teams: Assesses performance through DORA analysis and capacity audits
• DORA Analyst: Measures DORA metrics from engineering data sources
• Developer: Generates code using strict test-driven development, atomic pull requests, and docs
• Reviewer: Reviews pull requests for Definition of Done, code quality, security, and compliance
• Tech Debt Scout: Identifies and quantifies technical debt; code age, hotspots, complexity, TODO markers, and dependency health