Automate Initial Interview Scheduling with a Custom AI System

Most teams start with Calendly or the built-in scheduler in their ATS. Calendly links force the candidate to do the work, creating a poor experience for senior talent. Native ATS schedulers, like the one in Greenhouse, handle basic one-on-one calls but fail when trying to coordinate a three-person panel interview across different time zones.

A recruiter trying to schedule 20 initial screens sends out a scheduling link. Ten candidates book, but five others reply with specific availability like, "I can do next Tuesday after 3 PM EST." The scheduler cannot parse this, so the recruiter must manually check calendars and reply. The remaining five candidates never click the link, requiring manual follow-up that negates any time savings.

The fundamental issue is that these tools are rigid schedulers, not intelligent agents. They follow fixed rules and cannot understand conversational context. This forces recruiters to constantly monitor their inbox for exceptions, which defeats the purpose of automation and results in scheduling errors in over 15% of cases.

Syntora would begin an engagement by auditing your existing scheduling workflow. We would identify the necessary system integrations, typically involving OAuth 2.0 for read/write access to your team's Google or Outlook calendars and API keys for your Applicant Tracking System, such as Greenhouse or Lever. A crucial first step involves mapping your current process, from candidate outreach to sending the final calendar invite, to inform the agent's logic.

The core scheduling logic would be built in Python, using the Claude API for natural language understanding. This allows the system to accurately parse candidate availability from email replies, even with varied phrasing. We have experience building document processing pipelines using Claude API for financial documents, and the same pattern applies to extracting structured data from recruiting correspondence. This logic would be deployed as a serverless function on AWS Lambda. The function would cross-reference proposed times with the live availability of multiple interviewers' calendars via the Google Calendar API to find suitable slots.

Once a valid slot is identified, the system would draft a confirmation email and a calendar invite. We would use a Supabase database to maintain the state of each scheduling conversation, ensuring proper sequencing and preventing issues like double-bookings. If initial time slots do not work, the AI agent would intelligently suggest alternative times based on the interviewer's future availability.

The delivered system would also incorporate automated reminder emails sent prior to interviews. All agent actions would be logged using structlog for transparency and debugging. We would configure monitoring and alerting, such as CloudWatch alarms, to proactively notify your team via Slack if operational thresholds are exceeded, for example, if an API error rate increases or processing times become prolonged. This ensures ongoing system reliability and performance.