AI Automation for Recruitment Agencies

Our AI screening is designed to reduce bias, not introduce it. The system evaluates candidates against objective criteria you define: skills, qualifications, experience duration, and certifications. It does not consider name, age, gender, ethnicity, or suburb, factors that unconscious human bias often latches onto. We also run regular bias audits on the screening outcomes. If any protected characteristic correlates with screening decisions, we investigate and adjust the model. Australian anti-discrimination law (including the Fair Work Act, Age Discrimination Act, and Disability Discrimination Act) is built into our compliance framework.

Yes. We have deep integrations with both platforms. For JobAdder, we connect via their REST API covering candidates, jobs, placements, activities, and custom fields. For Bullhorn, we use the SOAP and REST APIs to manage candidates, job orders, submissions, and notes. The AI reads candidate data from your ATS, performs screening and matching, then writes results back, scores, notes, and status updates all appear in your existing workflows. We also integrate with PageUp, Lever, and Greenhouse for clients using those platforms.

Our AI does not replace phone screens entirely, but it handles the structured qualification questions that consume the first 10 minutes of every screening call. Availability, salary expectations, visa status, location preferences, notice period, and specific hard-skill verification can all be collected via an AI-powered conversation (text or voice) before a recruiter ever picks up the phone. The recruiter then starts their call already knowing the basics and can focus on the nuanced questions that actually require human judgement: cultural fit, career motivations, and soft skills.

Traditional ATS matching relies on keyword overlap between a resume and a job description, which is why candidates stuff their resumes with keywords. Our AI uses semantic matching: it understands that "P&L management" and "financial statement oversight" describe the same capability, that "5 years in SaaS" implies experience with subscription metrics, and that a candidate who managed a team of 20 is likely qualified for a role requiring team leadership of 10-15. This semantic approach typically increases relevant shortlist candidates by 40-60% compared to keyword matching.

Candidate experience is central to our design. Every automated touchpoint (application acknowledgement, screening questions, interview scheduling, and status updates) is personalised with the candidate’s name, the specific role, and relevant context. Candidates receive timely updates at every stage rather than the "black hole" experience that damages agency reputations. Our clients consistently report improved candidate NPS scores after implementing automation, because candidates prefer fast, transparent communication over waiting days for a human to manually send a status update.

Our automated reference checking sends a structured questionnaire to referees via email or SMS, tailored to the role and seniority level. Questions cover performance, reliability, teamwork, and role-specific competencies. Responses are compiled into a standardised report that highlights strengths, concerns, and any discrepancies with the candidate’s claims. For referees who prefer a phone conversation, the system schedules a call with a recruiter and provides a pre-populated discussion guide. All reference data is stored in compliance with the Privacy Act 1988 and only retained for the period specified in your privacy policy.