Syft for Greenhouse: AI Resume Parsing, Candidate Matching, and Faster Screening

Syft’s Greenhouse integration automates résumé and application screening by uploading job descriptions and candidate applications into Syft’s AI engine, extracting structured details from each submission, and ranking candidates against the job criteria. Once candidates are scored, recruiters can review top matches in Syft, adjust AI filters, or tag selected candidates to push them into Greenhouse’s candidate list. This article explains what the integration does, who benefits most from it, how to set it up, which metrics to track, practical best practices, and realistic limitations to consider before adopting it.

Core capabilities of the Syft–Greenhouse integration

• Auto-match against job descriptions Syft parses the uploaded job description and scores every applicant in your pool against that description using its custom AI model.
• Candidate-match (find similar candidates) Select a strong applicant and Syft returns a ranked list of similar candidates from your existing pool.
• Custom AI-based filters Beyond boolean searches, Syft offers granular, AI-informed filters based on parsed resume fields and contextual relevance.
• Tag-to-Greenhouse sync Tag candidates in Syft to have them automatically appear in your Greenhouse candidate list for next-stage workflows.
• Keyword search and media screening Traditional keyword search is supported, alongside tools for screening media and integrating assessments.

Who should consider this integration

• High-volume applicant pools Teams that receive hundreds to thousands of applicants per role and need to reduce manual screening time.
• Distributed recruiting teams Organizations where multiple sourcers and hiring managers must align on candidate shortlists quickly.
• Companies using Greenhouse as ATS Any company with Greenhouse that wants to add an AI-powered pre-screening layer before candidate import.
• Teams integrating assessments and media screening Recruiters who combine resume rankings with assessments or video screening to improve shortlist quality.

How the integration works (technical overview): Syft uploads the job description and applicant files from Greenhouse, parses free-text resumé content into structured fields (skills, roles, dates, education), and applies an AI ranking model tuned to your JD. Recruiters can view ranked lists in Syft, expand candidate profiles to see parsed highlights, apply AI-driven filters, and tag chosen candidates which triggers a sync back to Greenhouse.

Quick setup checklist

• Authorize connection Install the Syft app in Greenhouse and grant API permissions for job and candidate data exchange.
• Map job fields Confirm which Greenhouse job fields Syft should read (job description, hiring manager, location).
• Initial sync Let Syft ingest existing candidates and the target job description to generate an initial ranking.
• Configure filters Tune AI filter weights (experience, skills, keywords) and enable candidate-match rules as needed.
• Tag & validate Tag a small sample of top matches to push into Greenhouse and verify the candidate data mapping.

Key benefits recruiters can expect

• Faster shortlist creation AI ranking shifts time from screening low-fit applicants to interviewing better matches sooner.
• More consistent screening Parsing into structured fields reduces variability that comes from subjective manual scanning.
• Easier internal collaboration Tagged candidates in Syft appear in Greenhouse workflows, aligning sourcers and hiring managers.
• Flexible search beyond booleans AI filters surface contextual matches that simple boolean strings can miss.

Two practical use cases Example 1 — Volume hiring: A contact center hiring team receives 2,000 applications for 50 roles. Instead of manually scanning, Syft auto-matches applicants to each JD, returning a ranked shortlist of candidates who meet core criteria; recruiters then tag top candidates and push them into Greenhouse for phone screens. Example 2 — Passive internal sourcing: After hiring a standout candidate, a recruiter uses Candidate-match in Syft to find similar profiles within the applicant pool and bring them into Greenhouse for follow-up.

Best-practice configuration tips

• Start with accurate JDs AI ranking quality depends on a clear job description—define must-haves vs. nice-to-haves explicitly.
• Iteratively tune filters Adjust filter weights after reviewing 50–100 ranked candidates to reduce false positives.
• Validate with assessments Use assessments or structured interviews to confirm top matches rather than relying on score alone.
• Audit regularly Sample tagged candidates monthly to ensure the model’s outputs remain aligned with hiring outcomes.

Integration data flow and artifacts

Privacy, security, and compliance considerations: Before onboarding, review Syft’s privacy policy and Greenhouse support documentation to confirm data handling meets your organization’s compliance standards. Ensure API permissions are scoped appropriately, retain audit logs of sync events, and define retention rules for applicant data according to your legal requirements and internal policies.

Metrics and ROI framework to measure success

• Time-to-screen Compare average hours spent screening per role before and after integration; this directly measures time saved.
• Interview-to-hire ratio Track how many interviews are needed per hire to detect improvements in shortlist quality.
• Qualified candidate rate Measure percentage of candidates pushed to Greenhouse who pass initial screening or assessments.
• Cost-per-hire impact Estimate recruiter labor saved and calculate potential reduction in cost-per-hire over a defined period.

Limitations and when not to use Syft + Greenhouse: The integration is designed for structured résumé and application screening and performs best when there is sufficient applicant volume and clear job criteria. Small hiring programs with very specialized role requirements or where cultural fit is the primary selection criterion may find manual or interview-led sourcing more appropriate. Also, treat AI outputs as augmenting—never replacing—structured assessment and human judgment.

Common questions about the integration