> For the complete documentation index, see [llms.txt](https://dev.alvin.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dev.alvin.ai/overview.md). # Overview Getting started The Alvin Query Optimizer is built in pure, high performance [Rust](https://www.rust-lang.org/) and leverages [Apache DataFusion](https://datafusion.apache.org/) to parse and transpile SQL. Cool tech aside, the most important thing is the results we are achieving — on average saving our customers 52% on their BigQuery spend, automatically though: * **Query acceleration:** Our powerful query accelerator, built on Apache DataFusion, uses semantic analysis and logical plan rewriting to optimize your queries for maximum performance. * **Resource optimization:** Take advantage of the most cost-effective pricing model for every query. Alvin dynamically routes your queries to the cheapest and most efficient compute. * **Intelligent caching:** BigQuery's cache can be fragile. Alvin optimizes your workloads to maximise cache hits. More cache hits means near-instant results with zero cost. **How to get started?** Connect your BigQuery environment at connect.alvin.ai to generate a cost saving report. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://dev.alvin.ai/overview.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.