> For the complete documentation index, see [llms.txt](https://docs.rekor.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rekor.ai/rekor-one-r/readme/continuous-improvement.md). # Continuous improvement The Rekor One® Intelligence Engine operates on a powerful flywheel effect, propelling a cycle of continuous improvement that transforms data into knowledge for customers. This dynamic process involves the conversion of raw data into valuable insights, enriching the capabilities of the Intelligence Engine with each iteration. As customers leverage the knowledge gained from the system, this valuable feedback is seamlessly fed back into the system. This loop of constant refinement is further enhanced by the integration of machine learning, ensuring that the Rekor One Intelligence Engine evolves and adapts, offering increasingly sophisticated and effective solutions over time across all of our major markets—Urban Mobility, Public Safety, and Transportation Management. This flywheel effect is at the core of Rekor's commitment to providing cutting-edge roadway intelligence and underscores our dedication to staying at the forefront of technological advancements. ![Rekor One flywheel showing how data is continuously turned into knowledge and improves with every cycle](https://content.gitbook.com/content/E9q8OQ4yyo4SNj9YtGiU/blobs/3nlF5tC1WxP4TchZilMu/image) --- # 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.rekor.ai/rekor-one-r/readme/continuous-improvement.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.