Speak Integration for ATS: Automated Scoring, Two-Way Sync, and Screening Best Practices

Speak_ integrated with Greenhouse automates candidate evaluation by comparing resumes to role-specific criteria you define. The integration prioritizes applicants using customizable signals and synchronizes candidate status, enabling recruiters to move top-ranked candidates through Greenhouse without manual re-entry. This article explains how the Speak_–Greenhouse connection works, who benefits most, and how to set up signals, measure impact, and avoid common pitfalls so you can make faster, more accurate hiring decisions.

What the integration does: Speak_ ingests role descriptions and resumes, then scores applicants against weighted signals you choose. Scores and rankings are presented in Speak_, and candidates, notes, and status changes can be pushed to Greenhouse to maintain a single source of truth for pipeline progress.

Key features of the Speak_ + Greenhouse integration

• Custom evaluation signals Create unlimited signals that capture hard skills, soft skills, certifications, keywords, or other attributes and assign weights to prioritize what matters most for the role.
• Automated candidate scoring Resumes are compared against job criteria and signals, returning a ranked shortlist that highlights best-fit applicants for quicker screening.
• Two-way Greenhouse sync Move candidates through stages directly from Speak_, and maintain status updates in Greenhouse to keep recruiting teams aligned.
• AI-powered interview guides Automatically generate structured interview guides tailored to a candidate's score and the role's signals, reducing prep time for interviewers.
• Analytics and optimization Track signal performance and hiring metrics to refine weights, thresholds, and workflows over time.

Who needs this integration: recruiting teams that screen moderate to high volumes of applicants and want consistent, objective first-pass evaluations. Medium-sized companies (100–1,000 employees) and specialized hiring teams benefit most because they combine domain knowledge with enough hiring volume to justify signal customization and analytics.

Example signal sets by role (practical starting points)

• Software Engineer Signals: language proficiency (e.g., Python/Java), relevant experience years, open-source contributions, algorithmic problem examples, degree/certification. Weight high on technical skills and relevant experience.
• Account Executive Signals: quota attainment, CRM experience, industry vertical knowledge, communication samples, territory size handled. Weight quota attainment and CRM experience highest.
• Product Manager Signals: roadmap ownership, cross-functional leadership examples, metrics-driven outcomes, domain expertise, UX collaboration. Emphasize product impact and stakeholder management.

Sample signal weights for a mid-level Software Engineer

How it fits into your Greenhouse workflow: after linking accounts and mapping job templates, Speak_ will display ranked candidates for each Greenhouse job. Recruiters review the prioritized list, surface top matches, and can push chosen candidates to interview stages in Greenhouse. This preserves existing interview kits, scorecards, and approvals while reducing time spent on initial resume triage.

Implementation steps and best practices

• Connect accounts Create an API key in Greenhouse and connect it to Speak_ to enable two-way syncing.
• Define role templates Start with a standard job template in Speak_ mirroring Greenhouse job fields to ensure consistent evaluation.
• Create and test signals Build 5–8 initial signals, assign provisional weights, and run the model on a sample of recent hires to validate outcomes.
• Calibrate thresholds Set score thresholds for auto-reject, manual review, and interview shortlisting and iterate based on precision/recall tradeoffs.
• Train hiring teams Share how scores are generated and align interviewers on how to use Speak_ rankings alongside scorecards.
• Monitor and refine Use analytics to track signal performance, false negatives, and downstream interview conversion to refine weights monthly.

Metrics to track after deploying Speak_ with Greenhouse

Common questions about accuracy, customization, and data

Interview guides powered by Speak_ reduce interviewer prep and standardize evaluation. Based on candidate signals and job context, the AI builds a guide with role overview, tailored technical and behavioral questions, suggested time allocation, and scoring rubrics. Example: for a backend engineer, the guide can include a system design prompt, a debugging task, and competency questions linked back to the signals that drove the candidate score.

Common pitfalls and how to avoid them

• Over-weighting keywords Don’t let keyword presence dominate scoring. Combine keyword checks with evidence of ownership and outcomes to reduce false positives.
• Insufficient calibration Test signals against historical hires and tune weights before relying on automated thresholds.
• Ignoring edge cases Review low-scoring outliers; some strong candidates use unconventional resumes or come from nontraditional backgrounds.
• Skipping stakeholder alignment Involve hiring managers early so signals reflect real on-the-job priorities and buy-in is immediate.

Measuring ROI: start by benchmarking screening time, interview volume, and interview-to-offer ratios before roll-out. After deployment, expect measurable reductions in recruiter screening time, a tighter shortlist for interviews, and faster time-to-hire. Use the analytics in Speak_ and Greenhouse reports to quantify improvements and justify broader adoption across hiring teams.