The correct option is Low-code and no-code tools and pre-trained model APIs.

This choice matches the need to launch a simple website assistant in a few hours with minimal coding and without training models from scratch. Tools such as Dialogflow CX and Vertex AI Agent Builder let you design conversational flows through a visual console and quickly embed a web chat widget. You can connect to knowledge sources or FAQs about hours and tickets and rely on pre-trained models through Vertex AI so you avoid bespoke training and complex infrastructure. This approach is built for speed and simplicity while still providing enterprise integration and analytics if the assistant grows in scope.

Comprehensive MLOps and lifecycle management tooling focuses on experiment tracking, pipelines, model versioning, and deployment governance for teams that are building and iterating on custom models. It does not directly provide a quick way to stand up a working website assistant within hours.

Access to TPU and GPU hardware for bespoke model training is intended for training or fine tuning models and it requires significant time and engineering effort. This conflicts with the requirement to avoid heavy coding and to use existing capabilities.

Google AI research publications can offer valuable insights but they are not a managed product or service that you can deploy to build a functional assistant on a tight timeline.

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