> For the complete documentation index, see [llms.txt](https://docs.live-eo.com/surfacescout/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.live-eo.com/surfacescout/measuring-success.md). # Measuring Success Context We have developed a solution to address a common challenge for all our customers: mitigating risks around their pipelines. However, customers may perceive the quality of our analytics differently. Several factors can influence this perception, including the varied geographical environments that require fine-tuning of our machine learning model and QA processes—for example, the contrast between arid desert landscapes in the Middle East and lush, forested regions in Northern Europe. * **Geographies**

* **The level of interest can vary based on the type of detected change.** For instance, one customer might prioritize insights on agricultural or plowing activities differently than another. ### We need your feedback! We have showed you in the [Review Cases](/surfacescout/review-cases.md#reviewing-insights) section that you could evaluate the quality of an Insight:

This is essential to fine-tune the analytics so you can prioritize the risks that matter most to you, minimize false positives that create operational overhead, and assess whether SurfaceScout could become a valuable information source (we think it is! But we need your data to confirm it ;)). We typically track 3 key metrics: * **Precision**: The percentage of actual changes detected out of all predictions delivered. * **Relevance**: The percentage of relevant predictions out of all predictions delivered. * **Uniqueness of Information**: The number of unique detections not captured by other methods (like helicopters), compared to everything we’ve detected. * **Uniqueness & Relevance combined:** The number of unique and relevant detections compared to everything we've detected With your feedback, these metrics tend to improve significantly—often reaching over 80% relevancy, for example after a few deliveries. Without your input and domain expertise, however, results are less reliable (see the orange section for details).

{% hint style="info" %} For **Precision**, which measures the rate of false alerts, we typically begin around 90%. For **Relevance**, we usually start lower, around 40%. This is where your feedback makes a significant impact: after 4-6 deliveries, we often achieve scores above 80%! {% endhint %} {% hint style="info" %} For some customers, we can directly correlate detected Insights with tangible risk reduction in financial terms (€ or $). Recently, with a customer, we were able **to reduce risk exposure by 4M€ annualy for every 1,000 km of pipeline!** {% endhint %} --- # 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 dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.live-eo.com/surfacescout/measuring-success.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.