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

Abi**ram

Company:Cod**ityPosition:Co-FounderLocation:Seattle, Washington,United States

Conversation Record

Hi A***i, I’m J***e, an HR Recruiter at StrategyBrain. I came across your work co-founding C***y and building t***i,your experience in AI-driven test automation really stood out. We’re currently seeking a Principal AI Test Automation Engineer for N***I, where you’d architect scalable, machine-learning-powered testing platforms for large-scale systems. Would you be interested in a brief chat to explore this opportunity? Looking forward to your thoughts. Best, J***e

Hi J***e, thanks for reaching out. This Principal AI Test Automation Engineer role sounds exciting, especially with my background building t***i,I’d love to learn more about the tech stack and scale of N***I. When would be a good time to chat?

Hi A***i, Great to hear you’re interested! At N***I (a 200-500-person, engineering-driven startup), we’re building an enterprise-grade, AI-powered test automation platform that currently scales across thousands of microservices and processes millions of test runs per month. Here’s a bit more detail: • Tech stack: , Python and Java/Scala for core platform services , PyTorch/TensorFlow for ML-driven test case generation and analysis , Docker & Kubernetes on AWS for containerized deployments , CI/CD orchestration with Jenkins and GitLab CI • Your role: , Architect and own the end-to-end AI test automation framework , Integrate machine-learning models to optimize test coverage and execution speed , Collaborate with product, data science, and DevOps teams to set quality standards , Mentor a growing team of test engineers and evangelize best practices , Research and adopt emerging AI/testing tools to keep us at the cutting edge • Growth & impact: , Directly influence product quality for Fortune-level clients , Clear path to Director of Test Automation or Head of Quality Engineering , Equity package, competitive salary & bonus, flexible PTO, and learning stipend Does this align with what you’re looking for? If so, I’d love to dive deeper. I’m available Thursday 10:00,11:00 AM PST or Friday 2:00,4:00 PM PST,do either of those times work for you? Feel free to suggest alternatives or let me know if you have any other questions. Looking forward to chatting! , J***e

Thursday at 10:00 AM PST works great. Could you share how big the test automation team is today and what the top challenge for this role will be in the next quarter?

Hi A***i, Great,I’ve penciled you in for Thursday at 10:00 AM PST. Today, our dedicated test automation team is roughly a dozen engineers: a mix of ML specialists, platform developers, and senior test leads. Over the next quarter, your biggest challenge will be scaling our AI-driven test generation and analysis across an expanding set of microservices while maintaining low-latency integration in our Jenkins/GitLab CI pipelines. You’ll also be responsible for driving best practices across the team,mentoring engineers, refining our end-to-end test framework, and ensuring our models deliver reliable, actionable results at enterprise scale. We’ll cover this in more detail on Thursday, but feel free to let me know if you have any other questions in the meantime. Looking forward to our conversation! , J***e

Sounds great,two quick questions: what CI pipeline latency targets are you tracking today and how? And on the end-to-end test framework, are you expecting a greenfield rebuild or more incremental evolution of what’s in place?

Hi A***i, Great questions,here’s a high-level view before our call: 1) CI Pipeline Latency - We pull stage-level and end-to-end timings from both Jenkins and GitLab CI into our internal dashboards. - Our 95th-percentile target for a full regression run is ~10,12 minutes, while critical smoke suites aim for 2,3 minutes. - Alerts fire if any stage creeps above its SLAs, and we track historical trends to drive continuous optimization. 2) End-to-End Framework Strategy - We’re taking an incremental-evolution approach on top of our existing Python/Scala framework and orchestration layers. - You’ll iteratively replace and enhance modules with AI-driven test generators and analyzers, but there’s also room to design greenfield components for higher-level orchestration and reporting. Hope that helps,happy to dive deeper on Thursday at 10 AM PST. Let me know if there’s anything else you’d like to cover! , J***e

Thanks, J***e,that gives great context. Quick follow-ups: what observability/dashboard stack are you using to track those CI SLAs (Grafana, Kibana, custom)? And for the incremental framework work, how are module boundaries defined, and what metrics do you use to validate each AI-driven component before rolling it into prod?

Hi A***i, Great questions,here’s a bit more color: 1. Observability & Dashboards - We collect CI metrics (stage timings, success rates) via Prometheus and push logs through an ELK stack (Elasticsearch + Kibana) for deep dive. - Grafana sits on top of Prometheus for real-time SLA tracking (95th/99th pct). - We also maintain a lightweight custom React dashboard that aggregates test SLAs, alert history, and trending over time. 2. Incremental Framework Boundaries & Validation - We’ve broken our framework into four core modules,Test Generator, Orchestrator, Analyzer, and Reporter,each living in its own container with well-defined gRPC/REST APIs. - Before shipping a new/updated AI component, we gate it behind: • Generation throughput & latency tests • Model precision/recall on detecting flaky cases • Coverage uplift (Δ in branch/function coverage) • False-positive rate and end-to-end success rate on a synthetic “canary” suite • Resource (CPU/memory) overhead benchmarks - Any regression or SLA creep triggers automatic rollbacks. Hope this helps,happy to unpack more on Thursday at 10 AM PST! , J***e

Thanks J***e, that’s super helpful,just two quick ones: how do you manage the custom React dashboard (is it versioned and CI-gated like code)? And for your gRPC interfaces, what’s your versioning/backward-compat strategy to avoid compatibility hiccups when rolling out updates?

Hi A***i, Great questions,here’s how we handle both: 1. React Dashboard - We keep the dashboard in its own GitLab repo, fully versioned and CI-gated. Every PR runs ESLint, unit/component tests, a production build, and Docker image creation. Only once all checks pass does it get promoted from staging to prod. 2. gRPC Versioning & Backward-Compat - Our proto packages follow semantic versioning (e.g. myservice.v1, myservice.v2). We enforce additive-only changes on minor/patch releases (no renaming or reordering fields), and include automated compatibility checks in CI against existing clients. Breaking changes bump the major version, and both old and new services run side-by-side until consumers complete their migration. Hope that helps,let me know if there’s anything else before our call Thursday at 10:00 AM PST. Looking forward to diving deeper! Best, J***e

Thanks, J***e,two quick dives: on the React dashboard CI, do you enforce coverage thresholds or snapshot tests, and how do you handle urgent hotfixes outside the mainline release? And for gRPC major bumps, what’s your deprecation/migration process,do you provide tooling or automate stub regeneration to ease client upgrades?

Hi A***i, Great questions,here’s how we handle both: 1. React Dashboard CI - Coverage & Snapshots: We enforce a minimum 80% code coverage threshold on all UI components and run Jest snapshot tests against key views (charts, tables, forms). Any drop below threshold or unexpected snapshot diff fails the pipeline. - Hotfixes: For urgent fixes, we branch off a “hotfix” from the last tagged release, trigger a pared-down CI pipeline (lint, tests, snapshot diff) for rapid validation, then merge back into main and the active release branch. This keeps our mainline clean and ensures fix propagation without blocking standard PR reviews. 2. gRPC Major Bumps & Migration - Deprecation Process: When a breaking change is needed, we bump to a new major proto package (e.g., myservice.v2) and mark the older v1 methods as deprecated in docs and client code. Both versions run side-by-side for a configurable window (typically one quarter) so teams can migrate gradually. - Tooling & Stub Regeneration: We maintain an internal CLI/codegen plugin that pulls updated .proto files and regenerates client stubs (Java/Python/Go) automatically. Updated stubs are published to our artifact repo, and CI jobs run compatibility checks against consumer tests. This minimizes manual work on the client side. Looking forward to unpacking more on Thursday at 10:00 AM PST,feel free to shoot over any last-minute thoughts before then! Best, J***e

Thanks, J***e,quick follow-ups: for those Jest snapshots, how do you handle intentional UI changes vs. drift? Do you have an automated approval flow or visual diff tool in CI? And for your proto codegen plugin, is stub regeneration kicked off inside your main repo’s CI or managed as its own service?

Hi A***i, Great questions,here’s how we handle both: 1. Jest Snapshots - Any snapshot diff (intentional or drift) fails the CI job and surfaces a clear diff in GitLab’s merge request view. - We use a manual “Approve Snapshots” step: a reviewer inspects the diff, confirms intentional changes, and then runs `npm test --updateSnapshot` to commit the new baseline. - For higher-risk UI screens, we also gate against a parallel visual-diff check in CI,so any visual drift must be explicitly approved before the pipeline proceeds. 2. Proto Codegen Plugin - We keep our `.proto` files in a dedicated repo with its own CI pipeline. Whenever a proto change lands, our internal CLI/codegen service runs automatically in that pipeline, regenerates stubs, publishes updated artifacts, and kicks off compatibility checks. - Consumer repos then pull the new stubs as a dependency (or via auto-merge MRs), keeping the main application CI lean and decoupled from codegen. Looking forward to walking through this in our call Thursday at 10:00 AM PST. Let me know if there’s anything else you’d like to cover beforehand! Best, J***e

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.