HR & Recruiting

Hyper-personalized outreach and AI reply classification

Generate personalized outreach messages referencing a candidate’s skills, role, and projects, automate multi-step follow-ups, and use AI to classify inbound responses (Interested / Not Now / Wrong Person) to triage recruiter inboxes.

Why the human is still essential here

Recruiters oversee messaging strategy, ensure tone/ethics, and handle nuanced conversations; AI drafts and sorts at scale to free time for human interaction.

How people use this

AI-written first-touch outreach using profile signals

The system drafts individualized emails/InMails referencing the candidate’s tech stack, recent role, and notable projects while keeping to recruiter-approved templates.

Gem / ChatGPT

Automated multi-step follow-up sequences

If there’s no reply, the system schedules follow-ups and channel switches (email then LinkedIn) using performance-tested sequences at scale.

Lemlist / Reply.io

Reply intent classification and routing

Inbound responses are labeled (interested, not now, referral, unsubscribe) and automatically routed to the right recruiter workflow with CRM/ATS notes updated.

OpenAI / Zapier

Community stories (2)

LinkedIn

I have spent nearly three decades in talent acquisition, and one thing has always stayed true.

I have spent nearly three decades in talent acquisition, and one thing has always stayed true.

Recruiting works best when the systems behind it work.

Clear intake. Strong sourcing strategy. Clean workflows. These are the pieces that make the work feel doable instead of chaotic.


In healthcare especially, I have seen how easy it is for recruiters and sourcers to feel overwhelmed when roles are complex, pipelines are thin, and tools do not always keep up. I have also seen how the right process can change everything.


This is the space I love working in.

Intake. Sourcing strategy. Outreach. The small systems that create big wins.


I am also a strong believer in using AI to support the work we do. Not to replace recruiters, but to help us think more clearly, write more effectively, and move faster when it matters most. I use AI every day to build sourcing plans, draft outreach, clean up job postings, and bring structure to the parts of recruiting that often feel messy.


If you care about:

 building better sourcing systems

 improving intake

 modernizing how we approach hard-to-fill roles

 using AI without feeling overwhelmed

 making the day-to-day work feel lighter


You are in the right place.


I will be sharing more of what I have learned over the years. Practical strategies, prompts, sourcing ideas, and lessons that come from real experience in the trenches of healthcare recruiting.


If this resonates, I would love to connect and learn from your work too. Here is to building better systems together.

TD
Tammy DuranRecruiting and Sourcing Strategist
Mar 5, 2026
LinkedIn

I Built a Full Hiring Automation System And It Changes Everything for Recruiters

I Built a Full Hiring Automation System And It Changes Everything for Recruiters

Most organisations are still hiring the way they did 10 years ago manually sourcing profiles, reading every resume, sending copy-paste messages, and scheduling endless intro calls.


I designed a system that flips that entirely.


Here's how an end-to-end AI-powered hiring automation pipeline works — and why your recruiting team needs it NOW 👇


🔍 STEP 1 — Smart Sourcing (300–400 Profiles Per Role, Per Day)

I built the system to auto-generate Boolean search queries based on must-have skills, job titles, and dealbreakers — then pull 300–400 matching profiles daily from LinkedIn and Naukri directly into your ATS/CRM. No manual hunting. No missed talent.


🧠 STEP 2 — AI-Powered Screening (2-Layer Filter)

Every resume is parsed into structured data — skills, experience, companies, education, and seniority level — then scored using a two-layer approach I designed:

✅ Layer 1: Rules-based filtering (removes candidates missing must-haves instantly)

✅ Layer 2: AI Scorecard (GPT-4o / Claude) — evaluates skill match, project relevance, career stability and flags risk signals like job-hopping or skill gaps, with written reasoning and quoted evidence from the resume.

Recruiters only see candidates who are actually qualified.


✉️ STEP 3 — Personalized Outreach at Scale

Forget generic InMails. I integrated AI to draft hyper-personalized messages referencing each candidate's specific skills, current role, and standout projects — then run multi-step follow-up sequences automatically. Reply classification (Interested / Not Now / Wrong Person) is handled by AI so your team spends zero time sorting inboxes.


🎤 STEP 4 — Async AI Interviews (Before a Recruiter Ever Joins)

I added an async interview layer where interested candidates receive a chat or video interview link they complete on their own time. The system transcribes responses, scores competencies, flags weak answers, and pushes a full recommendation (Proceed / Hold / Reject) into the ATS — before a single recruiter minute is spent.


🛠️ The Tech Stack I Used

ATS/CRM: Greenhouse / Lever

Workflow Orchestration: n8n → Airflow

Data: Postgres + S3 + Pinecone (Vector DB)

AI Layer: GPT-4o / Claude + embeddings

Parsing: Affinda, Sovren, DaXtra

Outreach: Lemlist, Reply.io, Apollo

AI Interviews: HireVue or custom-built with Twilio + Whisper + LLM



The result? Recruiters stop doing administrative work and start doing what they're actually great at — building relationships and making great hiring decisions.


This isn't the future of hiring. It's happening right now.


🎥 Watch the full video breakdown above to see how each piece connects.


If you're in HR, Talent Acquisition, or building recruiting tech, drop a comment or DM me. Happy to share the full system design.


♻️ Repost if you think every recruiting team should see this.

SS
Shailove SinghAI Engineer
Feb 26, 2026