The full answer
For AI work, the stack adds models from Anthropic (Claude), OpenAI, Google (Gemini) and Meta (Llama), frameworks such as LangChain, TensorFlow and PyTorch, and vector databases such as pgvector and Pinecone.
These technologies are chosen because they are mature, widely used and well supported. That matters after launch: it is easier to hire engineers, find libraries, apply security updates and hand the product to an in-house team when it is built on mainstream tools.
Creuto does not force every project into the same template. A data-heavy analytics platform, a consumer mobile app and an enterprise integration project each have different needs, and the stack is selected to match. If your organisation already standardises on a technology, such as .NET or Java, Creuto can build within it.
Supporting tools matter as much as languages. Projects typically include Git-based version control, automated CI/CD pipelines, infrastructure as code, automated testing, error tracking and performance monitoring from the first sprint.
The proposed stack, and the reasoning behind it, is written into the proposal so you can review it with your own technical advisers before work begins.