AI Recruiter Conversation Demo - See How AI Engages Candidates on LinkedIn

1. To help you truly understand how our recruiter system works, we have selected real conversations from users using our AI recruiter on LinkedIn.
2. These examples show how our AI recruiter handles different scenarios.
3. All data has been strictly anonymized to protect personal information.

AI Recruiter Capabilities

What our AI Recruiter does in every conversation

7×24 Instant Reply

No timezone limits, responds within seconds anytime

Multi-language

Auto-detects language, supports 50+ languages

Intent Screening

Intelligently assesses candidate interest & willingness

Answer Questions

Answers role, salary, benefits inquiries automatically

Auto Follow-up

Proactive multi-round follow-up, zero manual effort

Schedule Interviews

Coordinates time slots & sends interview invitations

Get Contact Info

Naturally collects phone, email during conversation

Pra**der

Company:eTA** NZPosition:Software DeveloperLocation:Melbourne, Victoria,Australia

Conversation Record

Hi P***k, I’m J***e, an HR Recruiter at StrategyBrain. I came across your profile and was really impressed by your work at e***Z,especially how you streamlined student report generation and modernized a legacy codebase. We’re partnering with E***s, a fast-growing EdTech SaaS company, to hire a Full Stack PHP Engineer. Given your expertise in vanilla PHP, JavaScript, SQL optimization and GCP deployments, I believe this could be a strong match. Would you be interested in learning more about this role? Looking forward to your thoughts!

Hi J***e, thanks for reaching out and for your kind words. I’d be interested to learn more about the Full Stack PHP Engineer role,could you share a bit about the tech stack, team structure, and next steps?

Hi P***k, Great to hear you’re interested! Here’s a quick overview: 1. Tech Stack - Backend: vanilla PHP (8.x) - Frontend: JavaScript (ES6+), HTML5, CSS3 - Database: MySQL/PostgreSQL with optimized SQL queries - Cloud: G***e Cloud Platform (App Engine, Cloud SQL, Cloud Storage) - DevOps: Git-based workflows, CI/CD pipelines 2. Team Structure - A tight-knit agile squad of 6 , including 2 PHP backend engineers, 2 frontend developers, a QA engineer and a product manager - Collaborative culture with daily stand-ups, code reviews and shared ownership 3. Next Steps - 30-minute introductory call with me to discuss your experience, motivations and any questions - Technical interview with the Engineering Lead (focus on PHP/JS fundamentals and a quick coding exercise) - Final wrap-up chat with the Head of Engineering Would you be available for a 15,20 min call this week? Let me know your preferred slots and time zone, and I’ll send over a calendar invite. Looking forward to diving deeper! Best, J***e HR Recruiter, StrategyBrain

Thanks for the overview, J***e , the stack looks like a great match for my experience. Could you share what pain points the team is currently dealing with? I’m AEST and free Wednesday 3,5 pm or Thursday 10,12 if that works.

Hi P***k, Great question,here are the main pain points our team at E***s is tackling right now: 1. Legacy Code Modernization We’re on PHP 7 and need to refactor key modules to PHP 8, improving security, maintainability and testing coverage. 2. Performance & Scalability During peak hours, some report pages are slow,your SQL optimization experience would help us reduce query times and implement caching strategies for faster load. 3. Deployment Automation We still have a few manual deployments; we’re rolling out full CI/CD pipelines on GCP (Cloud Build, App Engine) to cut down on errors and accelerate release cycles. 4. Multi-Tenant Architecture As we expand to more schools, we need to ensure our database schemas and APIs scale seamlessly without impacting performance. 5. Third-Party Integrations We’re enhancing our G***e Drive file-upload flows and other EdTech API integrations for a more frictionless user experience. Given your background in refactoring legacy PHP, optimizing SQL, building CRUD APIs and GCP deployments, you’d hit the ground running on these challenges. Wednesday 3,5 pm AEST works perfectly,would 3:30 pm suit you? Let me know, and I’ll send over a calendar invite. If you’d prefer Thursday at 10 am, just say the word. Looking forward to chatting! Best, J***e HR Recruiter, StrategyBrain

Hi J***e, Wednesday at 3:30 pm AEST should work,please send over the calendar invite. I’ll review the pain points and any relevant docs beforehand so we can dive straight into specifics. Talk soon!

Hi P***k, Perfect,our 15-minute chat is set for Wednesday at 3:30 pm AEST. You’ll find the calendar invite (with Z***m details) in your inbox now. I’ve also attached a brief overview of our current architecture and pain points so you can dive straight into specifics. Let me know if you encounter any issues accessing the invite or docs. Looking forward to our discussion! Best, J***e HR Recruiter, StrategyBrain

Thanks for sending the docs, J***e. Quick question: what’s the on-call or after-hours support expectation,I’m used to fairly automated deployments at e***P and just want to gauge the load.

Hi P***k, Great question. At E***w we’ve built a pretty light on-call model to fit our automated deployments and GCP tooling: • Rotation: The six-person engineering squad shares a 24/7 on-call rota,each engineer typically covers one week every two months. • Frequency: Thanks to our CI/CD pipelines on Cloud Build/App Engine and proactive monitoring via Cloud Monitoring, we see very few after-hours incidents,averaging one or two non-critical alerts per month. • Scope: Most alerts are quick fixes (restarts, minor config tweaks) and can be handled remotely without full system rebuilds. For anything bigger, we have runbooks and team backups so you’re never tackling it solo. Overall, you’d spend the vast majority of your time focused on new features and performance optimizations during business hours, with only minimal, well-supported after-hours involvement. Does that align with what you’re used to at e***P? Happy to dive deeper if you have more questions. Best, J***e HR Recruiter, StrategyBrain

Thanks for clarifying,this sounds pretty similar to e***P’s setup. I’m curious though: how does the team handle post-incident reviews and keep runbooks up to date to avoid repeat alerts?

Hi P***k, Great question,and one we take very seriously at E***w. We run blameless post-incident reviews within 24,48 hours of any on-call alert. The engineer on duty walks us through the incident timeline, root cause and remediation, then we update our runbooks,stored in a Git-backed C***e space,with any new steps or improved alert thresholds. We assign clear owners for each section, and every quarter we audit the entire runbook library to retire outdated procedures. This continuous feedback loop is key to preventing repeat alerts. Your track record of automating reports and optimizing processes at e***P would be invaluable in refining our incident workflows. If this aligns with what you’re looking for, I’d love to discuss next steps,shall we set up a technical interview with our Engineering Lead? Let me know, or feel free to bring up any more questions before our Wednesday call. Best, J***e HR Recruiter, StrategyBrain

More AI Conversation Examples

See how our AI Recruiter handles different candidate responses and scenarios

Currently viewing: No job change intention — AI follow-up & rapport building

Interested — Shared Contact Info

Candidate expressed strong interest, AI successfully collected phone number and email for recruiter follow-up

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Not Now — Follow Up Later

Candidate is happy in current role but open to future contact, AI set a follow-up reminder

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Asking Questions — AI Answered

Candidate asked about salary, remote policy, and team size — AI provided accurate, real-time answers

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Declined — Not Interested

Candidate explicitly declined, AI responded professionally and preserved the relationship for future opportunities

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How AI Recruiter Communicates with Candidates

Our AI follows a proven 5-step workflow: Smart Outreach → Needs Discovery → Intelligent Q&A → Collect Information → Schedule Interview.

1 Smart Outreach

AI analyzes the candidate's LinkedIn profile — job title, skills, industry, and career trajectory — then generates a highly personalized first message that highlights how the role matches their background and aspirations. The AI proactively reaches out at optimal times and adapts the message tone to each candidate's seniority level.

2 Needs Discovery

Through multi-round natural dialogue, AI uncovers the candidate's real career needs — including desired role type, salary expectations, work location preferences, growth priorities, and reasons for considering a change. This insight helps match them with the most relevant opportunities.

3 Intelligent Q&A

AI automatically answers candidates' questions about the role — including job responsibilities, salary range, benefits, team structure, work setup (remote/hybrid/on-site), and company culture. Responses are accurate, context-aware, and delivered instantly in the candidate's preferred language.

4 Collect Information

For interested candidates, AI naturally collects key information during the conversation — phone number, email address, salary expectations, earliest start date, and availability. All data is structured and synced to the recruiter dashboard in real time.

5 Schedule Interview

AI coordinates the candidate's and interviewer's availability, proposes suitable time slots, sends interview invitations with meeting details (link, agenda, interviewer info), and automatically sends reminders before the interview to minimize no-shows.