AI-native product development built on Google technology.
WMITW develops its software products through an AI-assisted engineering process centered on Gemini in Google AI Studio. Applications are deployed using Firebase and Google Cloud infrastructure, creating a unified environment for prototyping, development, authentication, data management and scalable operation.
This technology foundation allows us to move quickly from an initial product concept to a working application while keeping infrastructure, product logic and user experience closely connected.
Gemini is integrated throughout our development workflow. It supports product exploration, system architecture, interface development, implementation, debugging, testing, documentation and ongoing iteration.
We use this AI-assisted approach to shorten development cycles and test product assumptions earlier. Final product decisions, architecture and quality control remain human responsibilities.
Vibe Coding describes an AI-assisted development process in which ideas, requirements and desired user behavior are translated into working software through close collaboration between a human product owner and an AI model.
At WMITW, Vibe Coding is not treated as an alternative to structured software development. It is used as an acceleration layer within a defined process that includes product strategy, architecture, validation, testing and review.
This makes it possible to build and improve specialized software with a small, focused setup while preserving a clear understanding of the product’s purpose, users and technical structure.
Our current development and infrastructure environment is based on:
Using one connected technology ecosystem reduces friction between product development, deployment and operation. Gemini and Google AI Studio support rapid product iteration, while Firebase and Google Cloud provide the managed infrastructure required to operate and scale digital applications.
This combination is particularly suited to WMITW’s development model: small, specialized product teams, fast validation cycles and multiple applications that share recurring infrastructure and product patterns.
AI-generated code is reviewed, tested and adapted before deployment. Sensitive configuration must be stored securely, access must follow the principle of least privilege and production data must not be exposed to development prompts or public environments.
Where products process personal or business-sensitive information, architecture and data flows must be documented and designed according to Privacy-by-Design principles.