vFunction

A full room and strong engagement at the AWS Transform Workshop in Santa Clara 💥 vFunction's Moti Rafalin presented to AWS teams, system integrators, and partners focused on accelerating application modernization, and the discussion didn’t disappoint. What stood out most was the quality of the conversation. Across the room, there’s a clear focus on reducing complexity, managing risk, and finding more effective paths to modernization. Thank you to the AWS and partner teams who helped make the session possible including Keith Groom, Rahul Chugh, Siddharth Mehta, Bhavye Sharma, Shilpa Andam, Joshua Wan and Rajesh R. Great to be part of these conversations and to continue working with AWS and partners to move modernization efforts forward. #AWS #ApplicationModernization #CloudTransformation #vFunction #AWSTransform

There’s a lot of discussion about AI code assistants. Less discussion about what an AI-assisted engineering workflow actually looks like in brownfield systems. In practice, it’s not “prompt → code → done.” It’s closer to: 1️⃣ Understand how the system behaves in production 2️⃣ Identify real domain boundaries and dependencies 3️⃣ Define safe refactoring targets 4️⃣ Use AI to generate changes within those constraints 5️⃣ Validate against runtime behavior, not just static code The shift is simple, but important: AI needs architectural context to be effective in complex systems. Without it, outputs are fast but inconsistent. With it, changes are aligned, repeatable, and safe to scale. At vFunction, we focus on providing that context i.e. reconstructing runtime behavior, dependencies, and domains so teams can guide AI-driven changes with a clear understanding of how the system actually works. Because in brownfield environments, generating code isn’t the challenge. Generating reliable, 𝒔𝒚𝒔𝒕𝒆𝒎-𝒂𝒘𝒂𝒓𝒆 change is. More in the comments!

In Seattle this week , vFunction and Evolve Cloud Services ☁ presented Modern Modernization: The New AWS Playbook, AND... what it actually takes to move from manual and AI-assisted approaches to architecture-aware, agent-driven transformation. Because the reality hasn’t changed: most cloud transformations still fail to capture expected value. What's changing: ➡️ AI paired with architectural context ➡️ Clear service boundaries driving refactoring ➡️ Real progress from monoliths to cloud-native systems The conversation 𝒊𝒔 shifting from migration to measurable modernization outcomes. Samantha, Shatanik and Moti Rafalin had great discussions with our AWS partners on what it takes to get there. Many thanks to Parvin Jabiyeva and Bhavye Sharma for their endless support. And yes, we even had Gen Alpha in attendance! The future of modernization is paying attention 👀

We’re excited to welcome Amir Rapson joining us at our Chief Architect Network London Event April 15–17 👑 Amir co‑founded vFunction and serves as its CTO, where he leads technology, product, and engineering. Prior to founding vFunction in 2017, Amir was GM and VP R&D of WatchDox (acquired by BBRY) until its acquisition by BlackBerry, where he also served as VP of R&D. Before that, he held R&D positions at CTERA and SofaWare (acquired by Check Point). Amir holds an MBA from IDC Herzlia and a BSc in Physics from Tel Aviv University. He's been a long standing support of our Chief Architect Network and we're excited to see him in London! Follow the Chief Architect Network to join live broadcast portions of this exciting event on Sofa Summits #SwitchOn CAN London on April 16th! https://lnkd.in/gzAUC4rs If you are a chief architect, apply to join your peers in our no-cost events: chiefarchitectnetwork.com

When 10M+ vehicles depend on your codebase, modernization becomes a high-risk decision. This case study shows what happens when a global automotive platform hits that wall: 11M lines of code 30K Java classes Dozens of tightly coupled domains Repeated attempts at service extraction stalled because they lacked a clear, system-derived understanding of how the architecture actually behaved in production. Instead of forcing another refactor, they stepped back and asked a better question: 𝐖𝐡𝐚𝐭’𝐬 𝐭𝐡𝐞 𝐬𝐢𝐦𝐩𝐥𝐞𝐬𝐭 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐰𝐞 𝐜𝐚𝐧 𝐮𝐬𝐞 𝐭𝐨 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐦𝐨𝐯𝐞 𝐟𝐨𝐫𝐰𝐚𝐫𝐝? What changed next is what matters: → Dozens of tangled clusters → 9 business-aligned domains → Guesswork → measured runtime architecture → Analysis → execution-ready modernization plan → Service-heavy model → AI-first execution And the outcome? 50% reduction in planned maintenance and operating costs. More below 👇