Automate Early-Stage Screening with Talent Llama and Greenhouse

Talent Llama’s Greenhouse integration automates the handoff between applicant tracking and conversational AI screening. It imports candidates from specified Greenhouse stages, invites them to generative-AI-led screening interviews, and pushes structured evaluations back into Greenhouse in near real-time. This setup replaces manual resume triage and many initial phone screens by delivering standardized, low-stress conversational interviews and enabling recruiters to advance or reject candidates from within Talent Llama while keeping Greenhouse as the single source of record.

At its core the integration does four things: import existing and new candidates from selected Greenhouse stages, send Talent Llama screening invites, push completed interview evaluations back to the candidate’s Activities tab in Greenhouse, and allow recruiters to change a candidate’s stage or reject them from the Talent Llama review screen. The import and evaluation sync are near real-time, with no partner implementation fee required.

Core features and how they help

• Automatic candidate import Select a job and one or more stages in Greenhouse and Talent Llama will import existing candidates in those stages plus any new candidates moved there.
• Conversational AI screening Generative AI conducts natural, interview-style conversations that probe competencies, reducing reliance on scripted phone screens.
• Near real-time result sync Completed screening evaluations are pushed to the candidate’s Activities tab in Greenhouse so hiring teams see results without manual entry.
• Stage transition from Talent Llama After reviewing an evaluation you can change the candidate’s Greenhouse stage directly from Talent Llama—no duplicate clicks across tools.
• Reject from review screen Reject a candidate in Greenhouse with a reason and optional note right from Talent Llama after evaluation, keeping workflow single-threaded.
• Low setup friction Connect a job in a few seconds and start importing—no partner implementation fee and English-language support out of the box.

Who benefits most? High-volume hiring teams, distributed recruiting operations, and roles with clearly defined competency requirements see immediate returns. Talent Llama reduces recruiter time spent on résumé skimming and first-round phone screens while delivering richer candidate responses than a checkbox assessment.

Supported regions, company sizes, and product notes

Recommended use cases

• High-volume intake roles Customer support, retail, or other roles where hundreds of applicants need consistent first-stage screening.
• Competency-focused technical roles Positions where structured behavioral responses or demonstration of problem-solving are more telling than resume keywords.
• Distributed/remote recruiting teams Teams spread across time zones can rely on asynchronous AI interviews to surface qualified candidates without scheduling delays.
• Structured hiring programs Internships, early careers, or graduate programs that require standardized screening across many applicants.

Practical integration workflow example: 1) Connect Talent Llama to Greenhouse and map the job. 2) Choose the Greenhouse stages to monitor and start import. 3) Talent Llama invites candidates to a conversational screening interview. 4) When interviews finish, assessments are pushed to Greenhouse Activities. 5) Recruiters open the Talent Llama evaluation, advance top candidates to the next stage or reject with a reason—all while Greenhouse remains the authoritative record.

Key metrics to track after deployment

• Time-to-screen Measure the reduction in recruiter hours spent on initial contact and phone screens per hire.
• Candidate throughput Track how many candidates are screened per recruiter per week before and after integration.
• Stage conversion rates Compare the percentage of screened candidates moved to interviews versus previous manual processes to detect quality changes.
• Interview completion rate Monitor the share of invited candidates who complete the Talent Llama conversational interview to optimize invite copy or timing.
• Time-to-hire Observe downstream effects on overall time-to-hire as early screening accelerates pipeline flow.

Common questions about the integration

Configuration checklist for a clean rollout: confirm API keys and permissions in Greenhouse, map Talent Llama job to the correct Greenhouse job, select target stages, prepare invite messaging and candidate instructions, create evaluation rubrics in Talent Llama aligned to your hiring criteria, and run a small pilot cohort to validate completion rates and evaluation quality.

Side-by-side comparison: Talent Llama + Greenhouse vs traditional methods

Security and operational considerations: ensure role-based access controls in both Talent Llama and Greenhouse, confirm retention and deletion policies for candidate interview recordings and transcripts, and validate that evaluation notes meet any internal audit requirements. Conduct a brief privacy impact assessment if you operate in regions with strict data protection rules.

Best practices for screening scripts and evaluations

• Define 3–5 core competencies per role Keep interview prompts focused on measurable behaviors and scenarios that map directly to evaluation rubrics.
• Use clear, candidate-friendly language Explain the conversational AI format in the invite and set expectations for length and types of questions.
• Train reviewers on rubric calibration Run a short calibration exercise so Talent Llama evaluations align with your hiring team’s standards.
• Monitor candidate completion patterns If completion drops, test different invite times, messages, or shorten the screening to reduce friction.

Limitations and things to watch for: conversational AI screenings are strong at eliciting structured responses but may not replace specialized technical assessments or live technical interviews that require code review or whiteboarding. Also, language support beyond English should be verified for roles recruiting in other languages. Finally, treat AI evaluations as an input to a human decision process rather than the sole determiner of fit.