Pequity + Greenhouse Integration: Embed Pay Bands, Peer Medians, and Approval Workflows into ATS Offer Process
Titus Juenemann •
July 11, 2025
TL;DR
The Pequity + Greenhouse integration embeds pay bands, peer medians, and automated approval workflows into the ATS offer process to create a single source of truth for compensation decisions. It speeds time-to-offer, enforces consistent approval policies, and provides audit-ready records while surfacing market context for recruiters and managers. Implementation requires job taxonomy mapping, approval-rule configuration, and short training, but yields measurable improvements in time-to-offer, approval turnaround, and offer acceptance. To maximize results, pilot, monitor key metrics, and maintain current benchmark data. Pairing Pequity’s compensation controls with tools that streamline candidate selection—like ZYTHR’s AI resume screening—further accelerates hiring velocity and improves outcome accuracy.
Pequity’s integration with Greenhouse connects compensation intelligence directly to your ATS workflows so teams make faster, more consistent pay decisions without scattering salary data across Slack, spreadsheets, and email. The integration centralizes ranges, peer medians, approval rules, and audit trails into a single source of truth that operates inside or alongside your Greenhouse offer flow. This article explains exactly what the integration does, which roles and company profiles benefit most, and the practical operational and metric-level advantages you can expect after deployment.
At a functional level the integration surfaces Pequity compensation data at decision points in Greenhouse—range checks when creating an offer, automated approval triggers when an offer sits outside configured bands or market medians, and contextual guidance for recruiters and managers during negotiations. It can also push offer outcomes back into Pequity for analytics and auditability.
Core features of Pequity + Greenhouse
- Range and peer data surfaced in ATS Recruiters and hiring managers see pay bands, level- and location-adjusted ranges, and peer median benchmarks while creating offers in Greenhouse.
- Automated approval flows Approval chains are triggered automatically based on where the proposed offer sits against configured ranges, peers, or custom thresholds.
- Single source of truth Comp decisions, approvals, and final offer details are stored centrally to maintain an auditable history and reduce duplicate records.
- Market trend tracking Pequity aggregates internal offers and external benchmarks so teams can see when ranges should be adjusted or when market movement affects competitiveness.
- Secure collaboration Rather than sharing sensitive pay information across unsecured channels, stakeholders collaborate in a permissioned Pequity environment linked to Greenhouse.
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Who benefits from this integration
- Recruiting teams Speed up time-to-offer and reduce back-and-forth by surfacing approval requirements and compensation guidance directly in the offer workflow.
- Hiring managers Make informed, consistent offers with immediate visibility into ranges and peer benchmarks for the role and location.
- Compensation analysts Standardize approvals, capture data for audits, and track market movement without manual data consolidation.
- Leadership and finance See approval logs and aggregate spend trends to control salary budgets and understand hiring cost trajectories.
- Companies with high hiring volume Organizations across sizes—from fast-growing startups to large enterprises—benefit where speed and consistency scale with volume.
How the integration works technically: Pequity typically connects to Greenhouse via API and embedded UI components. When a recruiter creates an offer, Pequity queries its compensation dataset and returns contextual recommendations and an approval state. If the offer triggers an approval rule (for example, an offer above the band midpoint or above peer median), Pequity generates an approval task and notifies approvers. Once approved, Pequity records the final offer and can push that outcome back to Greenhouse fields for reporting.
Typical data exchanged between Pequity and Greenhouse
| Data element | Purpose / Usage |
|---|---|
| Role, level, location | Match offer to the correct pay band and peer cohort for benchmarking |
| Range min / midpoint / max | Display and validate where an offer sits relative to the band |
| Peer median & external benchmarks | Provide market context for competitiveness checks |
| Offer amount and components | Trigger approvals and record final offer for analytics and audit |
| Approval status and audit log | Track who approved what and when for compliance and reporting |
Key operational benefits—beyond the obvious time savings—include: consistent application of pay policies, faster approvals when market conditions demand quick offers, fewer negotiation cycles due to better upfront guidance, and a consolidated dataset for compensation analytics. These outcomes reduce the risk of ad-hoc, inconsistent pay decisions and improve the predictability of offer success.
Representative workflows and use cases
- Hot-market fast offers Auto-escalate approvals for offers above midpoint to a senior approver with a short SLA so recruiters can close candidates quickly.
- Standardized approvals for high-skill roles Enforce additional review for specialized roles where pay decisions need extra oversight.
- Routine comp cycle adjustments Use Pequity’s market tracking to recommend band adjustments and then propagate those changes to offer workflows.
- Negotiation support Provide managers with market context and alternative offer structures (bonus vs. base) during candidate discussions.
Implementation considerations: expect an initial mapping exercise to align your job taxonomy and pay bands, configure approval rules, and assign permissions. Pequity lists no partner implementation fee in some contexts, but larger organizations often engage a partner or internal project resources for rollout. Plan for training sessions for recruiters and hiring managers and a short monitoring window post-launch to tune approval thresholds and data mappings.
Metrics to track after deployment
| Metric | Why it matters |
|---|---|
| Time-to-offer (days/hours) | Primary indicator of speed improvements from automated approvals and better guidance |
| Offer acceptance rate | Shows if offers are more competitive and aligned to market expectations |
| Approval turnaround time | Measures the effectiveness of automated approval chains and SLA configurations |
| Compensation variance vs. band | Monitors how often offers fall outside intended ranges and whether policy enforcement is working |
| Number of negotiation cycles | Lower cycles indicate better first-offer accuracy and fewer manual revisions |
Common questions about the integration
Q: Is candidate salary data stored in both systems?
A: Pequity is designed to store compensation decisions and audit logs centrally; Greenhouse will retain the offer fields needed for hiring workflow continuity. Sensitive handling and retention policies should be reviewed during setup.
Q: How are approval rules configured?
A: Approval rules are set in Pequity using thresholds (e.g., above max, above median) and can route to specific approvers or groups. Rules are customizable by role, level, and location.
Q: Will Pequity use external market data?
A: Yes—Pequity combines internal offer data with external benchmarks and peer medians to provide market context; configuration determines which sources are authoritative.
Q: Can approvers override Pequity recommendations?
A: Overrides are possible but should be governed by audit requirements. Pequity captures overrides and requires justification to maintain an auditable trail.
Q: Does the integration support enterprise-scale deployments?
A: Yes—Pequity supports organizations across a range of sizes and includes features for scaling approvals, governance, and analytics.
Q: What about language and regional support?
A: Pequity’s Greenhouse implementation is available in English by default; compensation datasets and regional configurations can be adapted by geography during implementation.
Best practices and governance: start with a conservative set of approval rules, run a pilot with high-volume teams, and use a change-control process for band updates. Document approval SLAs, assign clear owners for comp policy, and schedule periodic reviews of peer median sources and market benchmarks so the integration continues to reflect current realities.
Limitations and common pitfalls to avoid
- Stale or misaligned data If internal bands or external benchmarks aren't kept current the system will enforce outdated guidance—establish a cadence to refresh inputs.
- Over-automation Too many auto-approvals without clear escalation rules can reduce necessary human judgment in complex offers.
- Insufficient training Users unfamiliar with approval flows may bypass processes or delay offers; include role-based training and quick reference materials.
- Poor taxonomy mapping Misaligned job codes or locations lead to incorrect band matches—ensure a thorough mapping phase during setup.
Measuring ROI: a simple ROI example is comparing recruiter hours per offer before and after integration and translating time saved into cost. If automated approvals shave two hours off every high-touch offer and you do 1,000 such offers annually, that’s 2,000 recruiter hours redirected to sourcing and candidate care. Coupled with small improvements in offer acceptance rates from better-aligned offers, the integration often pays for itself through faster hires and reduced negotiation churn.
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