> ## Documentation Index > Fetch the complete documentation index at: https://www.dataframer.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Journey Findings > Find recurring patterns across whole user journeys, events and AI traces together **Journey Findings** looks for patterns that repeat across whole user journeys: the events and AI traces of one visit, one order, or one user's session, in the order they happened. [Findings](https://www.dataframer.ai/docs/findings/discovery-and-tracking.md) reads one AI trace at a time; Journey Findings reads the whole journey, so it can connect what the AI did with what the user did next, such as "the AI quoted a wrong shipping price, then the user left". Journey Findings works on the journeys your [linking rule](https://www.dataframer.ai/docs/user-signals/fields-and-linking.md#linking-rule) forms, across all your projects. Open it from **Journey Findings** in the sidebar. ## Discover patterns On the **Discovery** tab, choose **New discovery** and describe what to look for, or pick a ready-made question such as **Where did it go wrong for the user?**. You can also set: * **Quality**: Cheapest, Balanced, or High quality. Higher quality costs more. * **Journeys last active**: the last 7, 30 or 90 days, or all time. * **Events the journey must contain**, and **At least this many events**. * **Share of journeys to analyze**: analyze a sample to keep the cost down. * **Only journeys with AI traces** (on by default). Choose **Discover**. DataFramer drafts a few questions from yours, asks them about every journey, and groups the journeys into findings. Each finding has a name, a description, and the journeys in it; a finding that describes something going wrong is marked **Failure**. Open a finding to see its journeys, each with why it belongs there, and open a journey to see its full timeline. **Refresh** analyzes the journeys the session has not analyzed yet. If you edit the question or the filters, the current findings stay until the next refresh replaces them. ### When journeys are analyzed A journey is analyzed once it has had **no activity for 24 hours**, because events can arrive late: some pipelines load a warehouse only once a day. Before each run, DataFramer also imports new AI traces from every project, so the traces made near the end of a journey are included. If a project's imports keep failing, it stops holding runs back after 24 hours, and an amber box names it: "Could not import AI traces from project X since \[date]: \[reason]. Journeys analyzed since then do not include these traces." The box goes away once the project imports again. A journey larger than 25 million tokens is skipped; the status line under the session shows how many were skipped and why. ## Track a finding Open a finding and choose **Track**. A tracked finding keeps the question and filters it was found with, and every tracking refresh adds the new journeys that match it. Tracking never removes a journey and never creates new findings. On the **Tracking** tab: * Choose how often tracking refreshes: every hour, day, or week, or off. **Refresh now** runs it at once. * Each tracked finding shows its journey count over time and how many journeys the last refresh added. * Turn on the bell to get a Slack message when a finding gets new journeys. Connect Slack under **Settings** first. * **Unpin** stops tracking a finding. ## Changing the linking rule Changing the [linking rule](https://www.dataframer.ai/docs/user-signals/fields-and-linking.md#linking-rule) groups all your events and AI traces into journeys again, so findings would point at journeys that no longer exist. When you change it, DataFramer asks first, then: * keeps your discovery sessions, with their questions and settings, but clears their results; * deletes your tracked findings, with their alerts and trend history. Changing the paths of a field the rule groups journeys by does the same, and DataFramer asks first in the same way. These fields are the Journey id field, plus the match field (linking by a field) or the User field (linking by user and time gap). Run your sessions again once the journeys have been linked under the new rule. ## Customer-hosted deployments If your data stays in your own cloud account, Journey Findings runs in your data plane: the events and AI traces are read there, and the descriptions and explanations stay in your bucket; your browser reads them from your gateway. ## Next steps > **[User Journeys](https://www.dataframer.ai/docs/journeys/overview.md)** > > Browse a user's full timeline and the AI traces inside it > **[Fields and linking](https://www.dataframer.ai/docs/user-signals/fields-and-linking.md)** > > Choose how events and AI traces form journeys This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.