Ongig–Greenhouse Integration: Automate Job Description Optimization and Compliance

This guide explains how the Ongig integration for Greenhouse works, what it accomplishes, and which recruiting teams gain the most value. It focuses on practical setup, measurable outcomes, and operational best practices so you can evaluate fit quickly. You’ll get a step-by-step view of the integration flow (how job text moves between systems), the primary features to leverage (text optimization, templates, compliance scanning, analytics), and a checklist for implementation and early measurement.

At a high level, Ongig’s Text Analyzer connects to Greenhouse via API to pull job descriptions, analyze them for readability and language cues, and let you push optimized content back into the ATS. The integration is designed to be lightweight: job postings are edited in Ongig and synchronized back to Greenhouse without manual copy/paste. Key outcomes to expect are faster job description creation, consistent formatting through templates, and objective language analysis that highlights words and phrases for replacement. The integration also surfaces analytics you can use to measure clicks and engagement over time.

Core features of the Ongig–Greenhouse integration

• Text analysis engine Scores job descriptions on readability and flags gender-coded or role-coded language with suggested neutral alternatives.
• Greenhouse API sync Pulls job text from Greenhouse into Ongig for editing and pushes validated changes back to the ATS to maintain a single source of truth.
• Custom job description templates Create and store templates that match your ATS formatting so teams produce consistent postings faster.
• Compliance scanning Checks job text against location-based rules (e.g., pay-equity language requirements) and flags items that may need legal review.
• Analytics and reporting Tracks apply clicks and engagement metrics to quantify the impact of edits and support continuous optimization.

The integration operates through a standard API exchange: Ongig requests job data, analyzes text locally, and provides a UI for edits and template selection. Once edits are approved, Ongig uses the Greenhouse API to update the job posting, preserving job IDs and metadata so downstream ATS processes (workflows, approvals, distribution) remain intact. From a governance perspective, you map which Greenhouse users can trigger Ongig edits and whether updates require an internal approval step in the ATS. That mapping keeps audit trails clear and supports change control during rollout.

Typical integration workflow

Who should consider this integration

• High-volume recruiting teams Teams that publish many job openings and need to speed content creation while maintaining consistency.
• Centralized talent acquisition groups TA leaders who want template governance and measurable improvement in job posting performance.
• Legal or compliance teams Organizations that need automated scanning for location-based job posting rules (e.g., pay-equity disclosure).
• Recruiters focused on conversion Practitioners seeking incremental uplift in apply clicks through targeted language and readability improvements.

Measured results from deployments show tangible uplifts in candidate engagement: Ongig reports increases in apply clicks (up to ~13%) and specific improvements for female applicants in some cases (up to ~21%). Those are indicative figures—your mileage depends on baseline posting quality, volume of traffic, and A/B testing rigor. Use these metrics as operational KPIs: track apply click-through rate (CTR) before and after template rollouts, and run controlled experiments to isolate the effect of language changes versus distribution or employer brand updates.

Compliance and regional checks

Best practices to maximize value

• Build standard templates first Create role families and templates in Ongig that align with your Greenhouse job fields to reduce manual work.
• Run A/B tests Compare edited versus original postings on apply CTR and source quality to validate impact.
• Train stakeholders Give recruiters and hiring managers a short playbook on reading and applying suggestions from the Text Analyzer.
• Monitor analytics regularly Review apply clicks, time-to-fill, and source performance to detect when templates need refreshes.
• Preserve ATS metadata Ensure updates maintain job IDs, openings, and approval states so ATS reporting remains accurate.

Common implementation challenges are mostly operational: ensuring API permissions are correctly scoped, mapping fields so templates align with your Greenhouse configuration, and defining who owns final approvals. These are solvable with a short project plan and a few pilot jobs. Recommended mitigation: run a 4–6 week pilot with representative roles, collect analytic baselines, and iterate templates before full rollout. That reduces rework and highlights integration edge cases early.

Technical requirements & compatibility notes

FAQs about the Ongig–Greenhouse integration

A practical example: a recruiter drafts a Senior Engineer job in Greenhouse, pulls it into Ongig, applies the Engineering template, replaces a set of flagged words, and pushes the final posting back. Within two weeks they compare apply CTR and see improved engagement from target channels, then refine the template further based on analytics. That loop—draft, analyze, edit, publish, measure—is how teams lock in incremental gains while preserving HR governance and ATS data integrity.

Implementation checklist (quick)

• 1. Define owners Assign a project lead, template owner, and ATS admin for API configuration.
• 2. Configure API access Generate and secure Greenhouse API keys with appropriate scopes.
• 3. Build initial templates Create templates for top role families and validate field mapping in a sandbox.
• 4. Pilot 10–20 jobs Run edits on a representative sample and collect baseline metrics.
• 5. Roll out & monitor Deploy more broadly, provide quick training, and review analytics monthly.

In summary, the Ongig–Greenhouse integration is a practical tool for teams that want repeatable, data-driven improvements to job postings while keeping the ATS as the system of record. It streamlines text optimization, enforces template consistency, and provides measurable signals to guide iterative improvements. When implemented with a pilot-first approach and clear ownership, teams can expect faster job creation, clearer postings, and measurable uplifts in candidate engagement—backed by analytics to validate the results.