> For the complete documentation index, see [llms.txt](https://docs.coupler.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.coupler.io/sources/category/other/ashby/data-overview.md). # Data Overview Ashby exposes a wide range of recruiting data through Coupler.io — from top-of-funnel candidate sourcing all the way through to offer management. You can pull structured data about every stage of your hiring process and combine entities to build meaningful reports. ## Available entities | Entity | Best used for | | ------------------------- | ------------------------------------------------------------ | | Applications | Pipeline tracking, stage conversion rates, time-to-hire | | Candidates | Talent pool analysis, source attribution, diversity tracking | | Jobs | Open requisitions, headcount planning | | Job postings | Career page coverage, job board distribution | | Offers | Offer acceptance rates, compensation benchmarking | | Interview schedules | Interviewer workload, scheduling efficiency | | Feedback form definitions | Interview process consistency | | Archive reasons | Rejection analysis, pipeline quality | | Sources | Source-of-hire reporting | | Departments | Hiring by team or org unit | | Locations | Hiring by office or region | | Candidate tags | Talent segmentation | | Custom fields | Any additional structured data your team has configured | | Users | Recruiter activity and workload | ## Key fields by entity #### Applications | Field | Description | | -------------- | ---------------------------------------------------- | | Application ID | Unique identifier for the application | | Candidate ID | Links to the candidate record | | Job ID | Links to the job requisition | | Status | Current application status (active, archived, hired) | | Current stage | The interview stage the application is in | | Created at | When the application was submitted | | Updated at | Last update timestamp | | Archive reason | Reason for rejection if applicable | | Source | How the candidate was sourced | #### Candidates | Field | Description | | ------------ | ------------------------------ | | Candidate ID | Unique identifier | | Name | Full name | | Email | Primary contact email | | Phone | Contact phone number | | Tags | Applied candidate tags | | Created at | When the candidate was added | | Source | Origin of the candidate record | #### Jobs | Field | Description | | ---------- | ------------------------------- | | Job ID | Unique identifier | | Title | Job title | | Department | Associated department | | Location | Office or remote designation | | Status | Open, closed, or draft | | Created at | When the requisition was opened | #### Offers | Field | Description | | -------------- | ----------------------------------------- | | Offer ID | Unique identifier | | Application ID | Links to the application | | Status | Pending, accepted, declined, or rescinded | | Created at | When the offer was extended | #### Interview schedules | Field | Description | | -------------- | ------------------------------- | | Schedule ID | Unique identifier | | Application ID | Links to the application | | Start time | Scheduled interview start | | End time | Scheduled interview end | | Interviewers | Users assigned to the interview | ## Common field combinations These joins across entities are especially useful for recruiting analytics: * **Applications + Candidates + Jobs** — full pipeline view with candidate details and role context * **Applications + Sources** — source-of-hire analysis to see which channels produce the most applicants * **Applications + Archive reasons** — rejection analysis to understand where candidates drop off * **Offers + Applications** — offer acceptance rate by role, department, or source * **Interview schedules + Users** — interviewer workload and scheduling patterns Use the **Join** transformation in Coupler.io to combine these entities in a single data flow. ## Use cases by role {% tabs %} {% tab title="Talent acquisition" %} * Track pipeline conversion rates from application to offer by stage * Measure time-to-hire and time-in-stage for each job opening * Analyze which sources (job boards, referrals, events) produce the most hires * Monitor offer acceptance rates and spot patterns in declines {% endtab %} {% tab title="People & HR" %} * Report on hiring velocity by department and location * Track headcount growth against open requisitions * Audit archive reasons to identify bias or process gaps * Use custom fields data to capture and analyze structured interview feedback {% endtab %} {% tab title="Hiring managers" %} * See real-time status of candidates for their open roles * Review scheduled interviews and feedback form completion * Get a snapshot of how many candidates are at each stage of their pipeline {% endtab %} {% tab title="Finance & ops" %} * Forecast headcount costs using open jobs and offer data * Track recruiting spend per hire by combining source data with hiring outcomes * Align hiring timelines with budget cycles using requisition and offer timestamps {% endtab %} {% endtabs %} ## Platform-specific notes * The **start date** parameter filters records based on their creation or update timestamp — use it to limit large exports to a recent window * **Custom fields** appear as separate columns in your export; field names reflect what your team configured in Ashby * **Archive reasons** and **candidate tags** are lookup tables — join them to Applications or Candidates to get human-readable labels * User records include recruiters and hiring managers but may not reflect deactivated accounts in all cases * Ashby's API returns data in pages; Coupler.io handles pagination automatically so you always get complete results --- # Agent Instructions This documentation is published with GitBook. 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