The Quench.ai team moved fast as it built the platform, testing ideas and developing prototypes quickly to solve its customers' problems. This required scalable, performant, cost-effective infrastructure. However, as the company evolved, it became clear its third-party infrastructure didn't meet these criteria, and was unable to support the company's long-term growth.

As it searched for a new cloud provider, Quench.ai was impressed with the scalability, reliability, and speed of Google Cloud. What's more, with the Google for Startups Cloud Program, designed for scale-tier AI startups, Quench.ai could access credits to expand their infrastructure and grow their business without stretching resources. "We were impressed with the commitment of Google Cloud to building long-term relationships with startups," explains Kassai. "Having access to a dedicated account manager, even at an early stage, gave us confidence we had the right provider to scale with."

With fully managed services such as Cloud Run and Cloud SQL, Quench.ai can focus on developing its services without worrying about its infrastructure. Engineers use Cloud Run to easily deploy small, independent services for new features and rapid prototyping. For example, Quench.ai is using Cloud Run to develop a "handover agent" – a smart tool that makes it easier for employees to ask questions to get up to speed with their role when they've recently taken on a new job. 

Building on Cloud Run makes it easy for the Quench.ai team to experiment and bring new features to life, without having to manage complex architecture. "Cloud Run is very simple to use and lets you spring up new prototypes without the overheads of time or specific infrastructure knowledge," explains Nesek. "You don't have to invest in training to learn a more complex setup, you can just get moving straight away."

With Google Cloud AI services, Quench.ai is reducing its development time even further. With the help of Google AI Studio and Gemini 2.5, for example, Nesek was able to quickly generate a detailed blueprint for a custom debugging tool for internal use. Thanks to the large context window of Gemini 2.5, Nesek could add a large amount of system logs then ask Gemini to analyze the problem and design a tool to solve it. Nesek then used an AI coding agent to generate the code for this design before deploying the completed tool to Google Cloud as a secure Cloud Run service, complete with authentication and authorization through Identity-Aware Proxy. 

Previously, this process would've taken teams weeks from start to finish. With Google Cloud, Nesek managed to complete it in just two afternoons of coding and two hours for production deployment — a time reduction of more than 85%. This acceleration ensures Quench.ai can rapidly diagnose and resolve issues, consistently delivering accurate answers through their platform.

As Quench.ai looks to expand its use of Gemini 2.5 to give customers even faster access to relevant company data, it's also working on building agentic AI capabilities to fetch information from customers' systems in a smarter, faster way. This includes a sophisticated AI assistant that could actively search multiple unknown systems to find the right information to offer even clearer answers to customers' questions about onboarding and handover scenarios. "Having a large context window is helpful for this, as it allows you to send entire documents rather than trying to figure out which parts of the document are relevant," Nesek explains. "That's where we see Gemini 2.5 fitting in nicely."

For Kassai and Nesek, this rapid, innovative, and cost-efficient approach to development wouldn't be possible without Google Cloud. "The fact that we have never had to spend time worrying about our infrastructure has allowed us to focus on solving our customers' problems," Nesek explains. "At the same time, whenever we have needed a new service to build our product, we've never had to look further than Google Cloud."