KeyBank improves infra provisioning by 90% with Google Cloud; shrinks manual coding work with Gemini
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KeyBank improves infra provisioning by 90% with Google Cloud; shrinks manual coding work with Gemini
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*Featured in this video:* Azim Shaik, Lead Enterprise Architect at KeyBank
*Executive Summary:* KeyBank, one of the largest regional banks in the United States, partnered with Google Cloud to modernize its technology ecosystem. Using Google Kubernetes Engine (GKE), Google Cloud VMware Engine, Compute Engine, KeyBank improved infrastructure provisioning times by 90%, enabling developers to self-service compliant environments in minutes. KeyBank also adopted the Gemini platform to bring multimodal AI capabilities directly into developer workflows to help reduce routine friction — with one engineering team reducing a 600+ hour code refactoring project down to under 13 hours. To drive responsible, highly governed AI adoption in a strictly regulated environment, KeyBank built a retrieval-augmented generation (RAG) engine. Backed by a dedicated governance layer, this framework establishes strong guardrails to reduce risks while grounding models in institutional knowledge to deliver trustworthy AI that teams can rely on every day.
*Challenge:* Managing 200 years of banking history meant maintaining millions of lines of legacy code and manual processes. Developers spent significant time managing infrastructure and dealing with long lead times, which slowed experimentation and innovation. KeyBank wanted to modernize its foundations while also laying the groundwork to scale AI safely.
*Solution:* To eliminate infrastructure friction, KeyBank used Google Cloud VMware Engine to rapidly migrate workloads to the cloud, while deploying Google Kubernetes Engine (GKE) and Compute Engine to allow developers to build, test, and modernize applications in parallel. KeyBank also adopted the Gemini platform to accelerate software development, equipping engineers with multimodal AI capabilities to tackle everything from routine documentation to high-stakes code migrations. To deliver the highest levels of safety and security, KeyBank created a RAG engine on Google Cloud, supported by a dedicated governance layer. This architecture allowed KeyBank to establish clear guardrails to help mitigate risk while grounding models directly in its institutional knowledge to ensure every response is governed, accurate, and context-aware.
*Results:* Together, this foundation is driving new levels of operational agility across KeyBank. Teams can now self-service compliant environments in minutes — a more than 90% improvement — dramatically increasing productivity and enabling faster experimentation. With Gemini, engineers are using multimodal capabilities to tackle everything from routine documentation to high-stakes migrations, with one standout project shrinking over 600 hours of manual coding work to less than 13 hours. Backed by KeyBank's RAG engine, AI is now something teams trust and rely on daily, paving the way to pursue future innovation with confidence.
*Key takeaways and highlights from our interview with Azim Shaik, Lead Enterprise Architect at KeyBank:*
→ “Google Cloud VMware Engine allowed to migrate workloads quickly and safely, while GKE and Compute Engine enabled modernization in parallel without increased risk. We've reduced infrastructure provisioning down to minutes — a more than 90% improvement. Teams can self-service compliant environments in minutes instead of weeks. That dramatically increases developer productivity and enables faster experimentation.”
→ “We now have AI Assistance available from Google Cloud. We were able to document the legacy code in such a way that it is future ready. So we have up-to-date documentation to actually go back and fix. One of our engineering teams built a framework which handled our legacy process of moving codebases at KeyBank which usually takes 600+hours. We are now able to do the same thing in less than 13 hours.”
→ “We build a retrieval augmented generation engine on the Gemini platform, supported by a dedicated governance layer. This architecture allows us to ground Gemini in our specific institutional knowledge. Grounding models in our enterprise data makes AI outputs more accurate and context aware. It turns AI into something teams trust and rely on in real business workflows.”
*Google Cloud products used:* Google Cloud VMware Engine, Google Kubernetes Engine, Compute Engine, Gemini
*Learn more:*
→ KeyBank and Google Cloud https://cloud.google.com/customers/keybank
→ Azim Shaik, Lead Enterprise Architect at KeyBank https://www.linkedin.com/in/azimshaik/
*Executive Summary:* KeyBank, one of the largest regional banks in the United States, partnered with Google Cloud to modernize its technology ecosystem. Using Google Kubernetes Engine (GKE), Google Cloud VMware Engine, Compute Engine, KeyBank improved infrastructure provisioning times by 90%, enabling developers to self-service compliant environments in minutes. KeyBank also adopted the Gemini platform to bring multimodal AI capabilities directly into developer workflows to help reduce routine friction — with one engineering team reducing a 600+ hour code refactoring project down to under 13 hours. To drive responsible, highly governed AI adoption in a strictly regulated environment, KeyBank built a retrieval-augmented generation (RAG) engine. Backed by a dedicated governance layer, this framework establishes strong guardrails to reduce risks while grounding models in institutional knowledge to deliver trustworthy AI that teams can rely on every day.
*Challenge:* Managing 200 years of banking history meant maintaining millions of lines of legacy code and manual processes. Developers spent significant time managing infrastructure and dealing with long lead times, which slowed experimentation and innovation. KeyBank wanted to modernize its foundations while also laying the groundwork to scale AI safely.
*Solution:* To eliminate infrastructure friction, KeyBank used Google Cloud VMware Engine to rapidly migrate workloads to the cloud, while deploying Google Kubernetes Engine (GKE) and Compute Engine to allow developers to build, test, and modernize applications in parallel. KeyBank also adopted the Gemini platform to accelerate software development, equipping engineers with multimodal AI capabilities to tackle everything from routine documentation to high-stakes code migrations. To deliver the highest levels of safety and security, KeyBank created a RAG engine on Google Cloud, supported by a dedicated governance layer. This architecture allowed KeyBank to establish clear guardrails to help mitigate risk while grounding models directly in its institutional knowledge to ensure every response is governed, accurate, and context-aware.
*Results:* Together, this foundation is driving new levels of operational agility across KeyBank. Teams can now self-service compliant environments in minutes — a more than 90% improvement — dramatically increasing productivity and enabling faster experimentation. With Gemini, engineers are using multimodal capabilities to tackle everything from routine documentation to high-stakes migrations, with one standout project shrinking over 600 hours of manual coding work to less than 13 hours. Backed by KeyBank's RAG engine, AI is now something teams trust and rely on daily, paving the way to pursue future innovation with confidence.
*Key takeaways and highlights from our interview with Azim Shaik, Lead Enterprise Architect at KeyBank:*
→ “Google Cloud VMware Engine allowed to migrate workloads quickly and safely, while GKE and Compute Engine enabled modernization in parallel without increased risk. We've reduced infrastructure provisioning down to minutes — a more than 90% improvement. Teams can self-service compliant environments in minutes instead of weeks. That dramatically increases developer productivity and enables faster experimentation.”
→ “We now have AI Assistance available from Google Cloud. We were able to document the legacy code in such a way that it is future ready. So we have up-to-date documentation to actually go back and fix. One of our engineering teams built a framework which handled our legacy process of moving codebases at KeyBank which usually takes 600+hours. We are now able to do the same thing in less than 13 hours.”
→ “We build a retrieval augmented generation engine on the Gemini platform, supported by a dedicated governance layer. This architecture allows us to ground Gemini in our specific institutional knowledge. Grounding models in our enterprise data makes AI outputs more accurate and context aware. It turns AI into something teams trust and rely on in real business workflows.”
*Google Cloud products used:* Google Cloud VMware Engine, Google Kubernetes Engine, Compute Engine, Gemini
*Learn more:*
→ KeyBank and Google Cloud https://cloud.google.com/customers/keybank
→ Azim Shaik, Lead Enterprise Architect at KeyBank https://www.linkedin.com/in/azimshaik/
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