Arya by Leoforce — AI-driven sourcing integration for Greenhouse

This guide explains how the Arya by Leoforce integration with Greenhouse works, who should consider it, and the measurable benefits talent teams can expect. It focuses on objective capabilities, implementation factors, and practical steps to get results from the combined solution. You’ll find a feature breakdown, typical use cases, implementation and configuration tips, sample metrics, and common questions recruiters and hiring managers ask before deploying Arya inside Greenhouse.

What Arya delivers: an AI-powered sourcing and engagement layer that connects to Greenhouse and delivers a single, deduplicated, scored list of candidates directly into your ATS. The product aggregates a proprietary database (850+ million profiles sourced from 80+ channels across 150+ industries), applies a multiyear-evolved AI model that evaluates candidates across seven multidimensional data points and hundreds of attributes, and returns ranked matches in minutes. Integration specifics: Arya can push matched candidate lists into Greenhouse, sync contact details and candidate status, and supports candidate masking and contact enrichment. The integration is designed to shorten time-to-submit, increase pipeline volume, and surface candidates with higher predicted role compatibility.

Who should evaluate Arya + Greenhouse

• High-volume recruitment teams Teams that hire repeatedly for common roles (e.g., customer support, sales, software engineers) and need to reduce time-to-submit and scale sourcing operations.
• Hard-to-fill or niche skill searches Recruiters working on specialized technical or industry-specific roles benefit from Arya’s broad data coverage and attribute-based matching.
• Talent pipelining and proactive sourcing Organizations building talent pools for future hiring cycles can use Arya to surface passive candidates and maintain a ranked pipeline within Greenhouse.
• Teams seeking consolidated sourcing Companies that currently manage multiple sourcing channels and experience duplicate candidate records gain efficiency from Arya’s deduplication and unified results.
• Global or multi-lingual hiring operations Organizations hiring across regions benefit from Arya’s multi-region coverage and language support when integrated with a global ATS like Greenhouse.

Core features delivered by the integration

• Deduplicated, ranked candidate lists A single list of unique candidates, scored and ranked by predicted fit, delivered directly into the Greenhouse dashboard.
• Large aggregated profile database Access to a proprietary pool of 850+ million active and passive profiles aggregated from 80+ channels and 150+ industries.
• Rapid search-to-results Automated searches that return a prioritized candidate list in five minutes or less, lowering the time recruiters spend sourcing.
• Contact enrichment and up-to-date info Profiles include refreshed contact details where available, reducing manual research time when initiating outreach.
• Candidate masking options Configurable masking to remove non-role attributes from view during sourcing to focus evaluation on job-related signals.
• Pay-for-performance sourcing An optional model that drives AI-qualified applicants to prioritized roles and aligns cost with delivery of candidates for high-need positions.

Side-by-side: Manual sourcing vs Arya + Greenhouse

How the matching and ranking work (practical view): Arya ingests job requisition details, applies profile filters, and evaluates candidate relevancy across seven multidimensional data points — for example: skills and certifications, role history and tenure, career trajectory and mobility signals, demonstrated performance indicators, contactability and activity, contextual industry fit, and engagement propensity. These dimensions are combined into a composite relevancy score and then used to rank and deduplicate candidates. From a workflow perspective, recruiters configure role parameters in Arya, review the ranked list, and then trigger a push to Greenhouse. Candidates appear as sourced profiles with score metadata, and enrichment fields synchronize back to the ATS for outreach and tracking.

Top use cases where the integration produces the most ROI

• Rapid scalability for hiring surges When headcount ramps are scheduled, delivering a pre-ranked candidate pool reduces sourcing lag and enables faster submittals.
• Niche technical roles For roles with specific skill matrices, attribute-based AI matching finds candidates beyond common keyword matches.
• Continuous pipelining Maintain evergreen candidate pools for hard-to-fill functions and keep Greenhouse populated with qualified prospects.
• Closing contactability gaps Reduced time spent finding current contact details thanks to built-in enrichment.
• Reducing sourcing fragmentation Consolidates results from multiple paid and free channels into one view inside Greenhouse, lowering administrative overhead.

Common implementation and operational questions

Expected metrics and how to measure success: Arya advertises a reduction in time-to-submit by up to 50% through consolidated searches and automated ranking. Practical KPIs to track after deployment include time-to-submit, time-to-interview, quality-of-submission (hire rate from Arya-sourced candidates), pipeline velocity, and recruiter sourcing hours saved. Set baseline metrics before activation and measure changes over successive hiring cycles to quantify ROI. Also track integration-specific metrics such as sync error rates, fields mapped successfully, and the proportion of Arya-sourced candidates that convert to interviews and offers to validate the model’s alignment with your roles.

Best practices to maximize value from Arya + Greenhouse

• Define clear role profiles Provide precise requisition requirements (core skills, must-have vs nice-to-have, level, location flexibility) to improve match relevance.
• Tune scoring and filters Adjust weighting across dimensions (experience, skills, recency) for different job families rather than using a single global setting.
• Use deduplicated lists for outreach Leverage the deduplicated, ranked lists as the single source of truth to avoid repetitive outreach to the same candidates from multiple channels.
• Monitor outcome metrics Regularly review hire rates and time metrics and iterate on role definitions and scoring rules.
• Coordinate recruiter workflows Train recruiters on how to interpret Arya scores in Greenhouse and standardize when to push candidates forward in the hiring funnel.

Integration capabilities and where they apply

Limitations and situations to evaluate carefully

Pricing and procurement considerations: Arya offers different commercial approaches including a pay-for-performance option for driving applicants to high-need roles. While there may be no partner implementation fee, evaluate total cost of ownership by considering subscription or per-delivery fees, expected reductions in recruiter hours, and conversion rate improvements. Ask the vendor for a pilot or proof-of-value with measurable KPIs before committing to a longer-term contract. Procurement should include a trial period, clear SLAs for integration reliability, data privacy review, and a plan for measuring outcomes relative to your established baselines.

Implementation checklist and suggested timeline: start with stakeholder alignment (recruiting, hiring managers, IT), map Greenhouse fields and permissions, configure role templates and scoring rules in Arya, run a controlled pilot on a subset of roles for 4–6 weeks, measure outcome metrics (time-to-submit, interview rate, hire rate), iterate on scoring and filters, then scale to other teams. Key governance items include defining who can push candidates to Greenhouse, how masking is applied, and weekly review of candidate quality during the pilot period to validate model behavior and ensure consistent recruiter adoption.