> For the complete documentation index, see [llms.txt](https://karini-ai.gitbook.io/karini-ai-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://karini-ai.gitbook.io/karini-ai-documentation/introduction.md).
# Introduction
Karini's Agentic AI foundation platform empowers users to build, deploy and monitor end-to-end generative AI applications with a few simple clicks. The following diagram illustrates how Karini AI delivers a comprehensive [Agentic AI foundational platform](https://www.karini.ai/insights/navigating-genaiops-in-enterprises) encompassing the entire application lifecycle. This platform delivers a holistic solution that accelerates time to market and optimizes resource utilization by providing a unified framework for development, deployment, and management.
* **Access to Cutting-Edge LLMs and VLMs:** Seamlessly integrate state-of-the-art models from Amazon Bedrock, Amazon SageMaker, Databricks, Azure OpenAI, Google Vertex, and private model providers.
* **Prompt Engineering:** Create straightforward or agentic prompts using templates, evaluate them across different models for latency, cost, and quality, and maintain a record of historic runs.
* **Knowledge Bases:** Connect to enterprise data sources, object stores, business applications, databases, or data warehouses to build or access knowledge bases in real-time.
* **No-Code Recipes:** Deploy agents, multi-agent systems, generative BI, RAG, or batch workflows in minutes using intuitive no-code recipes.
* **Deploy and Collect Feedback:** Easily deploy agents as chatbots with customizable styles, feedback settings, and audio controls.
* **Observability:** Offers built-in tracing and tracking of all conversations to construct a semantic knowledge base, analyze performance and cost trends, and export conversations for model fine-tuning.
## Installing Karini AI
* [Pre-requisites](/karini-ai-documentation/installation.md#pre-requisites)