We believe in delivering value through innovation, efficiency, and expert leadership. Our mission is to empower businesses with scalable technology strategies, drive digital transformation, and optimize operational performance to stay competitive in a rapidly evolving landscape.
Our experience spans a range of high-profile leadership roles in companies such as Lone Wolf Real Estate Technologies, eXp Realty, and Black Knight Financial Services. We’ve successfully:
Led by Stephen Ledwith, MBA, a seasoned technology executive with an extensive background in engineering leadership, product management, and enterprise operations, our firm provides top-tier expertise in:
Led by Stephen Ledwith, MBA, a seasoned technology executive with an extensive background in engineering leadership, product management, and enterprise operations, our firm provides top-tier expertise in:
Welcome to the Blog — where technology leadership meets real-world execution. We cut through the noise with actionable insights on scaling teams, innovation, and strategy. No fluff. Battle-tested expertise from industry leaders who’ve built and led high-performing teams. Ready to level up? Explore our latest posts now.
Artificial Intelligence (AI) is reshaping the modern technology landscape. In autonomous engineering teams, AI offers incredible opportunities to enhance CI/CD, governance, team structures, and overall productivity. However, adopting AI requires caution and awareness of potential pitfalls. Let’s explore how AI can improve each area we’ve discussed so far—and where teams should tread carefully.
As engineering teams embrace autonomy, the risk of shadow IT—unauthorized technology solutions that bypass official channels—can grow. This article explores how to balance autonomy with visibility, governance, and trust.
Autonomous teams often innovate faster, but this freedom can lead to hidden or unauthorized solutions, known as shadow IT. According to Gartner, shadow IT can represent 30% to 40% of IT spending in large enterprises (Gartner Report).
Supporting autonomous engineering teams requires a strong foundation of CI/CD, Infrastructure as Code (IaC), and automation tools. This article explores real-world tools, best practices, and integration strategies to help your teams scale effectively.
Modern engineering teams thrive when they can deploy, test, and iterate rapidly. Autonomous teams especially need robust pipelines and infrastructure that support independence without sacrificing governance or stability.
Autonomous engineering teams are reshaping the way technology organizations deliver value. Let’s explore real-world case studies from leading tech companies, showcasing both successes and challenges, to understand the impact of scaling autonomous teams.
Spotify’s engineering culture is often cited as the blueprint for autonomous teams. Their Squad model empowers small, cross-functional teams to own specific features end-to-end. Squads align with Tribes (related groups), Chapters (discipline-based communities), and Guilds (interest-based communities) to balance autonomy with knowledge sharing.
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