> For the complete documentation index, see [llms.txt](https://docs.rainbird.ai/rainbird/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rainbird.ai/rainbird/getting-started/readme.md). # What is Rae? From the team behind Rainbird, Rae is the platform for building and running decision models your agents can use, and you can explain. Large Language Models (LLMs) excel at language understanding and prediction, providing compelling narrative that sounds and looks right. But they should not be making your decisions, because every response is probabilistic, based on unknown training data and cannot be explained. Rae turns **your** knowledge - policies, regulations, expertise - into a decision model. The reasoning engine uses it to make decisions consistently, with evidence for every outcome. Use Rae to automate decisions that must be right every time. Examples include eligibility checks, underwriting, claims triage, and compliance sign-off. Given the same facts, Rae always reaches the same decision. It shows the exact rules and evidence behind it. Build a model of the decision you want to automate, then integrate it into the processes, products or services your customers already use — so every decision they rely on is faster, consistent, and explainable, at whatever volume you need. ### How it works Rae works in three stages: **codify, reason, prove.** 1. **Codify** — Capture your policy and expertise as a decision model. 2. **Reason** — The Reasoning Engine applies that knowledge deterministically to reach a decision. 3. **Prove** — Every decision ships with an evidence tree. It contains the rules, facts, and certainty behind it. People and machines can read it. The Rae Decision Automation Platform provides the tooling and support to build, test & iterate over decision models - keeping subject-matter-experts in the loop - before publishing and managing these as decision agents in your solutions. **Got 2 minutes? Watch our Rainbird explainer video.** {% embed url="" %} ### Integrate Rae into your systems **Building** * **Claude Skill** — Use Claude to research, build, and live-test decision models via Claude and Claude Code. **Reasoning** * **APIs & SDKs** — Call a decision model directly from your applications. * **MCP** — Publish a decision model as an MCP tool. Your AI agents can call it and receive a decision with its evidence tree. * **3rd-party connectors** — Automation platforms including UiPath, Blue Prism, and Microsoft Power Automate. Wondering what you can do with Rae? Want to integrate automated decisions into your business? See our use case examples for inspiration. {% content-ref url="/pages/lUq2XATe7VjJXvP1eOM9" %} [Example use cases](/rainbird/getting-started/use-cases.md) {% endcontent-ref %} --- # 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://docs.rainbird.ai/rainbird/getting-started/readme.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.