Added use cases section in the docs

apps/webapp/app/v3/services/aiRunFilterService.server.ts

@@ -30,7 +30,7 @@ const AIFilterResponseSchema = z
 export interface QueryQueues {
   query(
     search: string | undefined,
-    type: "task" | "custom" | undefined
+    type: "task" | "custom" | undefined,
   ): Promise<{
     queues: string[];
   }>;
@@ -39,14 +39,14 @@ export interface QueryQueues {
 export interface QueryVersions {
   query(
     versionPrefix: string | undefined,
-    isCurrent: boolean | undefined
+    isCurrent: boolean | undefined,
   ): Promise<
     | {
-        versions: string[];
-      }
+      versions: string[];
+    }
     | {
-        version: string;
-      }
+      version: string;
+    }
   >;
 }
 
@@ -64,13 +64,13 @@ export interface QueryTasks {
 
 export type AIFilterResult =
   | {
-      success: true;
-      filters: TaskRunListSearchFilters;
-    }
+    success: true;
+    filters: TaskRunListSearchFilters;
+  }
   | {
-      success: false;
-      error: string;
-    };
+    success: false;
+    error: string;
+  };
 
 export class AIRunFilterService {
   constructor(
@@ -80,7 +80,7 @@ export class AIRunFilterService {
       queryQueues: QueryQueues;
       queryTasks: QueryTasks;
     },
-    private readonly model: LanguageModelV1 = openai("gpt-4o-mini")
+    private readonly model: LanguageModelV1 = openai("gpt-4o-mini"),
   ) {}
 
   async call(text: string, environmentId: string): Promise<AIFilterResult> {
@@ -92,7 +92,9 @@ export class AIRunFilterService {
           lookupTags: tool({
             description: "Look up available tags in the environment",
             parameters: z.object({
-              query: z.string().optional().describe("Optional search query to filter tags"),
+              query: z.string().optional().describe(
+                "Optional search query to filter tags",
+              ),
             }),
             execute: async ({ query }) => {
               return await this.queryFns.queryTags.query(query);
@@ -110,22 +112,27 @@ export class AIRunFilterService {
                 .string()
                 .optional()
                 .describe(
-                  "Optional version name to filter (e.g. 20250701.1), it uses contains to compare. Don't pass `latest` or `current`, the query has to be in the reverse date format specified.  Leave out to get all recent versions."
+                  "Optional version name to filter (e.g. 20250701.1), it uses contains to compare. Don't pass `latest` or `current`, the query has to be in the reverse date format specified.  Leave out to get all recent versions.",
                 ),
             }),
             execute: async ({ versionPrefix, isCurrent }) => {
-              return await this.queryFns.queryVersions.query(versionPrefix, isCurrent);
+              return await this.queryFns.queryVersions.query(
+                versionPrefix,
+                isCurrent,
+              );
             },
           }),
           lookupQueues: tool({
             description: "Look up available queues in the environment",
             parameters: z.object({
-              query: z.string().optional().describe("Optional search query to filter queues"),
+              query: z.string().optional().describe(
+                "Optional search query to filter queues",
+              ),
               type: z
                 .enum(["task", "custom"])
                 .optional()
                 .describe(
-                  "Filter by queue type, only do this if the user specifies it explicitly."
+                  "Filter by queue type, only do this if the user specifies it explicitly.",
                 ),
             }),
             execute: async ({ query, type }) => {
@@ -142,12 +149,15 @@ export class AIRunFilterService {
           }),
         },
         maxSteps: 5,
-        system: `You are an AI assistant that converts natural language descriptions into structured filter parameters for a task run filtering system.
+        system:
+          `You are an AI assistant that converts natural language descriptions into structured filter parameters for a task run filtering system.
 
   Available filter options:
   - statuses: Array of run statuses (PENDING, EXECUTING, COMPLETED_SUCCESSFULLY, COMPLETED_WITH_ERRORS, CANCELED, TIMED_OUT, CRASHED, etc.)
   - period: Time period string (e.g., "1h", "7d", "30d", "1y")
-  - from/to: ISO date string. Today's date is ${new Date().toISOString()}, if they only specify a day use the current month. If they don't specify a year use the current year. If they don't specify a time of day use midnight.
+  - from/to: ISO date string. Today's date is ${
+            new Date().toISOString()
+          }, if they only specify a day use the current month. If they don't specify a year use the current year. If they don't specify a time of day use midnight.
   - tags: Array of tag names to filter by. Use the lookupTags tool to get the tags.
   - tasks: Array of task identifiers to filter by. Use the lookupTasks tool to get the tasks.
   - machines: Array of machine presets (micro, small, small-2x, medium, large, xlarge, etc.)
@@ -159,7 +169,7 @@ export class AIRunFilterService {
   - scheduleId: Specific schedule ID to filter by
 
 
-  Common patterns to recognize:
+  Common workflows to recognize:
   - "failed runs" → statuses: ["COMPLETED_WITH_ERRORS", "CRASHED", "TIMED_OUT", "SYSTEM_FAILURE"].
   - "runs not dequeued yet" → statuses: ["PENDING", "PENDING_VERSION", "DELAYED"]
   - If they say "only failed" then only use "COMPLETED_WITH_ERRORS".
@@ -232,7 +242,9 @@ export class AIRunFilterService {
       }
 
       // Validate the filters against the schema to catch any issues
-      const validationResult = AIFilters.safeParse(result.experimental_output.filters);
+      const validationResult = AIFilters.safeParse(
+        result.experimental_output.filters,
+      );
       if (!validationResult.success) {
         logger.error("AI filter validation failed", {
           errors: validationResult.error.errors,
@@ -252,7 +264,9 @@ export class AIRunFilterService {
           from: validationResult.data.from
             ? new Date(validationResult.data.from).getTime()
             : undefined,
-          to: validationResult.data.to ? new Date(validationResult.data.to).getTime() : undefined,
+          to: validationResult.data.to
+            ? new Date(validationResult.data.to).getTime()
+            : undefined,
         },
       };
     } catch (error) {

docs/docs.json

@@ -333,6 +333,16 @@
               }
             ]
           },
+          {
+            "group": "Use cases",
+            "pages": [
+              "guides/use-cases/overview",
+              "guides/use-cases/data-processing-etl",
+              "guides/use-cases/media-generation",
+              "guides/use-cases/media-processing",
+              "guides/use-cases/marketing"
+            ]
+          },
           {
             "group": "Example projects",
             "pages": [

docs/guides/introduction.mdx

@@ -15,6 +15,8 @@ mode: "center"
   <Card title="SvelteKit" img="/images/logo-svelte.png" href="/guides/community/sveltekit" />
 </CardGroup>
 
+import UseCasesCards from "/snippets/use-cases-cards.mdx";
+
 ## Guides
 
 Get set up fast using our detailed walk-through guides.
@@ -39,6 +41,8 @@ Get set up fast using our detailed walk-through guides.
 | [Using webhooks in Next.js](/guides/frameworks/nextjs-webhooks)                            | Trigger tasks from a webhook in Next.js                              |
 | [Using webhooks in Remix](/guides/frameworks/remix-webhooks)                               | Trigger tasks from a webhook in Remix                                |
 
+<UseCasesCards />
+
 ## Example projects
 
 Example projects are full projects with example repos you can fork and use. These are a great way of learning how to use Trigger.dev in your projects.

docs/guides/use-cases/data-processing-etl.mdx

@@ -0,0 +1,159 @@
+---
+title: "Data processing & ETL workflows"
+sidebarTitle: "Data processing & ETL"
+description: "Learn how to use Trigger.dev for data processing and ETL (Extract, Transform, Load), including web scraping, database synchronization, batch enrichment and more."
+---
+
+import UseCasesCards from "/snippets/use-cases-cards.mdx";
+
+## Overview
+
+Build complex data pipelines that process large datasets without timeouts. Handle streaming analytics, batch enrichment, web scraping, database sync, and file processing with automatic retries and progress tracking.
+
+## Featured examples
+
+<CardGroup cols={3}>
+  <Card
+    title="Realtime CSV importer"
+    icon="book"
+    href="/guides/example-projects/realtime-csv-importer"
+  >
+    Import CSV files with progress streamed live to frontend.
+  </Card>
+  <Card title="Web scraper with BrowserBase" icon="book" href="/guides/examples/scrape-hacker-news">
+    Scrape websites using BrowserBase and Puppeteer.
+  </Card>
+  <Card
+    title="Supabase database webhooks"
+    icon="book"
+    href="/guides/frameworks/supabase-edge-functions-database-webhooks"
+  >
+    Trigger tasks from Supabase database webhooks.
+  </Card>
+</CardGroup>
+
+## Benefits of using Trigger.dev for data processing & ETL workflows
+
+**Process datasets for hours without timeouts:** Handle multi-hour transformations, large file processing, or complete database exports. No execution time limits.
+
+**Parallel processing with built-in rate limiting:** Process thousands of records simultaneously while respecting API rate limits. Scale efficiently without overwhelming downstream services.
+
+**Stream progress to your users in real-time:** Show row-by-row processing status updating live in your dashboard. Users see exactly where processing is and how long remains.
+
+## Production use cases
+
+<CardGroup cols={1}>
+  <Card title="MagicSchool AI customer story" href="https://trigger.dev/customers/magicschool-ai-customer-story">
+
+Read how MagicSchool AI uses Trigger.dev to generate insights from millions of student interactions.
+
+</Card>
+
+<Card title="Comp AI customer story" href="https://trigger.dev/customers/comp-ai-customer-story">
+
+Read how Comp AI uses Trigger.dev to automate evidence collection at scale, powering their open source, AI-driven compliance platform.
+
+</Card>
+<Card title="Midday customer story" href="https://trigger.dev/customers/midday-customer-story">
+
+Read how Midday use Trigger.dev to sync large volumes of bank transactions in their financial management platform.
+
+</Card>
+</CardGroup>
+
+## Example workflow patterns
+
+<Tabs>
+  <Tab title="CSV file import">
+    Simple CSV import pipeline. Receives file upload, parses CSV rows, validates data, imports to database with progress tracking.
+
+<div align="center">
+
+```mermaid
+graph TB
+    A[importCSV] --> B[parseCSVFile]
+    B --> C[validateRows]
+    C --> D[bulkInsertToDB]
+    D --> E[notifyCompletion]
+```
+
+</div>
+  </Tab>
+
+  <Tab title="Multi-source ETL pipeline">
+    **Coordinator pattern with parallel extraction**. Batch triggers parallel extraction from multiple sources (APIs, databases, S3), transforms and validates data, loads to data warehouse with monitoring.
+
+<div align="center">
+
+```mermaid
+graph TB
+    A[runETLPipeline] --> B[coordinateExtraction]
+    B --> C[batchTriggerAndWait]
+
+    C --> D[extractFromAPI]
+    C --> E[extractFromDatabase]
+    C --> F[extractFromS3]
+
+    D --> G[transformData]
+    E --> G
+    F --> G
+
+    G --> H[validateData]
+    H --> I[loadToWarehouse]
+```
+
+</div>
+  </Tab>
+
+  <Tab title="Parallel web scraping">
+    **Coordinator pattern with browser automation**. Launches headless browsers in parallel to scrape multiple pages, extracts structured data, cleans and normalizes content, stores in database.
+
+<div align="center">
+
+```mermaid
+graph TB
+    A[scrapeSite] --> B[coordinateScraping]
+    B --> C[batchTriggerAndWait]
+
+    C --> D[scrapePage1]
+    C --> E[scrapePage2]
+    C --> F[scrapePageN]
+
+    D --> G[cleanData]
+    E --> G
+    F --> G
+
+    G --> H[normalizeData]
+    H --> I[storeInDatabase]
+```
+
+</div>
+  </Tab>
+
+  <Tab title="Batch data enrichment">
+    **Coordinator pattern with rate limiting**. Fetches records needing enrichment, batch triggers parallel API calls with configurable concurrency to respect rate limits, validates enriched data, updates database.
+
+<div align="center">
+
+```mermaid
+graph TB
+    A[enrichRecords] --> B[fetchRecordsToEnrich]
+    B --> C[coordinateEnrichment]
+    C --> D[batchTriggerAndWait]
+
+    D --> E[enrichRecord1]
+    D --> F[enrichRecord2]
+    D --> G[enrichRecordN]
+
+    E --> H[validateEnrichedData]
+    F --> H
+    G --> H
+
+    H --> I[updateDatabase]
+```
+
+</div>
+  </Tab>
+</Tabs>
+
+<UseCasesCards />