Overview
Cached Jobs are the execution units for your brand monitoring. When you create queries, MentionLab generates cached jobs that run against multiple AI models to track how your brand is mentioned in AI-generated responses.Cached Job Properties
| Property | Type | Description |
|---|---|---|
id | string (UUID) | Unique identifier |
project | string (UUID) | Parent project ID |
query_id | string (UUID) | Associated query ID |
query | string | The search term being monitored |
aiModels | string[] | AI models used for analysis |
executionCount | number | Number of executions per run |
language | string | ISO 639-1 language code |
country | string | ISO 3166-1 alpha-2 country code |
status | string | Current job status |
createdAt | string | Creation timestamp |
updatedAt | string | Last update timestamp |
Job Status
Cached jobs progress through different states:| Status | Description |
|---|---|
pending | Job is queued and waiting to execute |
active | Job is currently running |
ended | Job (job-executions) has completed - may contains failure, check job-executions |
AI Models
Cached jobs can run against multiple AI providers:| Model Key | Description |
|---|---|
openai | OpenAI GPT-4 models |
openai5 | OpenAI GPT-5 models |
anthropic | Anthropic Claude models |
google-generative-ai | Google Gemini models |
perplexity | Perplexity AI |
xai | xAI Grok models |
ai-overview | Google AI Overviews |
ai-mode | AI search modes |
How Cached Jobs Work
1
Query Creation
When you create a query, MentionLab automatically generates a cached job for it.
2
Scheduling
Based on your project’s recurrence settings, jobs are scheduled to run at specified intervals.
3
Execution
The job runs against selected AI models, capturing responses that mention your brand.
4
Analysis
Results are analyzed and stored as Result Analysis records for you to review.
Execution Count
TheexecutionCount property determines how many times each job runs per scheduled execution. Higher counts provide more comprehensive data but consume more credits.