Megan Automates Resume Screening, Candidate Re-Engagement, Interview Scheduling, and AI Summaries in Greenhouse

Mega HR’s Megan is an AI hiring partner that runs natively inside Greenhouse to automate screening, outreach, scheduling and interview summarization while keeping Greenhouse as the single source of truth. The integration syncs jobs, candidates, stages, interviews and notes in real time so teams don’t need to change their tools or core workflows to get immediate automation benefits. This guide explains what the Mega HR—Greenhouse integration does, which teams will benefit most, the measurable benefits you can expect, and practical steps for implementation, monitoring, and troubleshooting.

What the integration does in practical terms: Megan screens incoming resumes with configurable criteria, re-engages passive or prior candidates at scale, schedules interviews with calendar sync, captures interview notes and recordings, and writes concise summaries and recommendations back into Greenhouse. It also runs continuous audits for bias and system behavior to provide visibility into fairness and risk.

Who should consider adding Megan to Greenhouse

• High-volume hiring teams Companies handling large applicant volumes (campus hiring, retail, customer service) where manual triage consumes recruiter time.
• Distributed interview teams Organizations with multiple hiring managers and interviewers who need consistent screening, summaries, and centralized communication.
• Resource-constrained startups Small recruiting teams that need to scale outreach, screening and scheduling without adding headcount.
• Enterprise hiring operations Large employers that require audit trails, compliance-grade logging and role-based controls while reducing time-to-fill.

Key features at a glance

• AI screening & shortlisting Automated resume review using configurable criteria; surfaces highest-priority candidates and flags mismatches to reduce false negatives.
• Bi-directional Greenhouse sync Jobs, stages, candidate records, interviews and notes sync in real time so Greenhouse remains the source of truth.
• Autonomous scheduling Full Google and Microsoft calendar sync with custom scheduling flows—Megan can manage bookings automatically.
• Interview summaries and insights AI-generated notes and recommendations saved back to Greenhouse, plus optional recording attachments and scoring.
• Continuous auditing Ongoing bias and behavior audits with dashboards for hiring ops and compliance teams.

Manual approach vs. Megan-powered Greenhouse

How it works technically: the Mega HR app installs into your Greenhouse account and establishes bi-directional API sync. Jobs and candidate metadata are mirrored so Megan can read job criteria, stage flow and historical notes. Actions Megan takes—screening decisions, messages, calendar events, interview summaries—are written back to the candidate record and mapped to Greenhouse stages, preserving audit trails and visibility for hiring teams.

Implementation checklist (quick rollout)

• Map stakeholders Identify recruiting, hiring managers, IT/security and HR owners who will approve configuration and access.
• Define screening criteria Document must-have, nice-to-have, and excluded attributes for initial roles to train Megan’s shortlisting logic.
• Configure calendar and email sync Connect Google/Microsoft calendars and set default scheduling windows and interviewers.
• Pilot with one job family Run a 4-week pilot on 5–10 requisitions to validate outcomes and tweak scoring thresholds.
• Monitor and iterate Review audit logs, candidate feedback, and shortlisting precision; adjust rules and filters.

Metrics to track after deployment: time-to-first-screen (how long until candidate is triaged), time-to-interview, interview-to-offer ratio, recruiter hours saved per requisition, candidate response rates on re-engagement campaigns, and the precision/recall of shortlists compared to manual review. These KPIs quantify the integration’s operational impact and help justify further scaling.

Best practices for set-up and governance

• Start with conservative thresholds Use Megan to surface candidates for human review rather than auto-reject on day one; increase automation as confidence grows.
• Preserve recruiter review windows Allow recruiters to override AI decisions and flag edge cases to improve training signals.
• Log changes and decisions Keep an audit trail of rule changes, remediation actions and sample reviews for compliance and iterative improvement.
• Train stakeholders Hold short sessions with hiring managers on how Megan scores candidates and what the summary outputs mean.

Security, privacy and compliance controls

Common questions from hiring teams

Real-world illustrative scenarios: a 200-person scale-up cut recruiter screening time by roughly 60% within a month by having Megan pre-score applicants and manage scheduling. An enterprise operations team reduced interview scheduling overhead by 75% across 50 weekly interviews by enabling calendar sync and automated booking rules. These examples show how time savings compound across requisitions and hiring cycles.

Pitfalls to avoid and troubleshooting tips

• Over-automation too early Avoid auto-reject rules until you’ve validated precision; start with suggestion workflows and build trust.
• Not aligning score criteria Ensure job descriptions and Megan’s scoring rules reflect real hiring priorities; mismatches lead to poor shortlists.
• Ignoring feedback loops Capture recruiter overrides and false positives as training data; use them to refine filters and rules.
• Under-monitoring audits Regularly review audit dashboards for unexpected screening patterns or system errors and set alerts.

Conclusion: Mega HR’s Megan brings practical automation into Greenhouse without forcing tool changes, delivering measurable time savings, consistent candidate handling and maintainable audit trails. Teams that combine conservative rollout, clear governance and iterative measurement typically achieve the best outcomes—speeding hiring while preserving human judgment.