Candidate Screening Enhancements

This year, we’ve heard it loud and clear from our customers: the #1 priority is finding high-quality candidates for their open positions. QUALITY OVER QUANTITY So, we’ve been hard at work refining how social media ads drive applications – not just more of them, but better ones. Here's what we've done 👇 Introduced logic-based application experiences to improve the quality of candidates from social media ads. Here’s how it works: as candidates begin the application process, they’re asked critical screening questions—like “Do you have 5 years of nursing experience?” If this a job requirement and they select “no,” the process redirects them away from applying to the specific role and instead guides them to explore other opportunities on the company’s career site that may be a better fit. This approach ensures that only candidates meeting key requirements move forward, while others are seamlessly directed to more suitable roles, saving time for recruiters and improving overall candidate quality. The result? We're seeing a huge improvement of hire rate! One customer is seeing a 4% higher rate of quality candidates – significantly outperforming their previous benchmarks.

The old hiring model is officially broken. Companies post jobs and get buried under thousands of generic applications. Recruiters spend entire days screening candidates who looked good on paper but clearly didn't read the job description. Meanwhile, great candidates get lost in the noise. But what if we flipped the process on its head? What if AI handled the repetitive screening so you could focus on the candidates who actually matter? That's exactly what we built with Elly's AI Interviewer. Instead of drowning in applications, you get ranked results. Instead of calendar chaos, you get automatic scheduling. Instead of scattered notes, you get clear candidate summaries. Here's how it works: → Connect your ATS → AI generates screening questions from your job description → Candidates get interviewed automatically → You get scored, summarized results 1,000 applications become 10 qualified candidates. 72 hours of screening becomes 2 hours of decision-making. The best part? Candidates actually prefer it. They get to tell their full story without rushing through a 15-minute human screener who's already mentally moved on to the next call. We're not replacing human judgment. We're giving it back to you. Because the future of hiring isn't about processing more applications faster. It's about finding the right people without losing your mind in the process.

🎯 Just dropped Part 4 of my learning journey through "Exploring Good Old-Fashioned AI" module from Cornell University and eCornell's "Designing and Building AI Solutions" Program (Instructed by Lutz Finger): https://lnkd.in/grhxwf-u The plot twist? I applied my learning to build and test 16 DECISION TREE MODELS as supervised classifiers to assist recruiters in candidate screening for my TalentSol applicant tracking system (ATS) learning project. The results from AI model evaluations at optimized decision thresholds and target recall 70% based on testing/hold-out dataset are surprising: 📊 10/16 models achieved target 70% recall for candidate screening 🚀 XGBoost & Hybrid Stacking hit 57% precision at 70% recall ⚡ HashingVectorizer ensembles: 73% recall, 53% precision 🔥 Simple averaging ensemble nearly matched complex stacking But here's what REALLY surprised me... For the problem statement and use case of text matching between resume text and job descriptions, traditional vectorizers (Count, TF-IDF, Hashing) matched sophisticated word embeddings (BERT, ModernBERT) while being: • Significantly more interpretable • Faster to deploy • Explainable to hiring managers, regulators and auditing parties The killer insight? In regulated industries and high-stakes hiring decisions, the interpretability of decision trees is an advantage to have. Full technical deep-dive with code, evaluation metrics, production insights and data-driven approach to identify target recall with marginal tradeoffs for precision: https://lnkd.in/gKddBrbt What's your take? Are we over-engineering AI when simpler solutions can deliver better business value for specific, well-understood problems? CC: Cornell University, eCornell, Cornell Johnson Graduate School of Management, Lutz Finger #MachineLearning #DecisionTrees #CoreAI #AIImplementation #HRTech #CornellUniversity #eCornell

Imagine screening hundreds of candidates’ digital footprints — accurately — in minutes. Today, many HR teams still rely on manual social media screening — a process that is slow, inconsistent, and risky. Manually reviewing profiles takes hours per candidate. It introduces human bias. It depends heavily on the reviewer’s judgment and stamina. And it leaves companies vulnerable to missing critical risks that could lead to bad hires or reputational damage. The consequences of manual vetting are serious. Important signals get missed. Different reviewers interpret content differently, creating inconsistencies. Candidates accept other offers before background checks are completed. And when red flags surface too late, it’s not just an operational setback — it becomes a legal, cultural, and brand risk. In today’s hiring environment, where speed, fairness, and trust are critical, outdated processes quietly become major liabilities. AI is fundamentally reshaping how organizations approach social media screening. With AI, HR teams can: - Screen hundreds of candidates in minutes, not days. - Apply standardized, bias-reducing risk detection across every profile. - Analyze content in context — distinguishing genuine risks from irrelevant noise. - Uncover hidden behavioral patterns that manual reviews often miss. - Move faster without compromising on thoroughness or fairness. By pairing AI’s speed and consistency with human judgment, organizations can build safer, smarter, and more resilient hiring processes. At Phyllo, we’re proud to help forward-thinking teams navigate this shift — enabling more confident, ethical, and efficient screening practices for the future of work. Is your organization still relying on manual social media screening? How are you rethinking your hiring processes today?

I just automated HR Talent Aquisition. Are you spending countless hours sifting through resumes? HR professionals spend an average of 𝟮𝟯 𝗵𝗼𝘂𝗿𝘀 screening resumes for a single hire. I built an AI-powered HR assistant that reduces screening time from 𝟮𝟯+ 𝗵𝗼𝘂𝗿𝘀 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿 𝟲𝟬 𝘀𝗲𝗰𝗼𝗻𝗱𝘀, while enhancing the quality of candidate evaluations through consistent, evidence-based insights. 𝗜𝗻 𝗺𝘆 𝗹𝗮𝘁𝗲𝘀𝘁 𝗮𝗿𝘁𝗶𝗰𝗹𝗲, 𝗜 𝗰𝗼𝘃𝗲𝗿: 1️⃣ 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗿𝗲𝘀𝘂𝗺𝗲 𝗮𝗻𝗮𝗹𝘆𝘇𝗲𝗿 using Azure OpenAI Assistants & Semantic Kernel. 2️⃣ 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗼𝘃𝗲𝗿 𝟭,𝟬𝟬𝟬 𝗿𝗲𝘀𝘂𝗺𝗲𝘀 𝗺𝗼𝗻𝘁𝗵𝗹𝘆 𝗳𝗼𝗿 𝗹𝗲𝘀𝘀 𝘁𝗵𝗮𝗻 $𝟭 in API costs. 3️⃣ 𝗣𝗿𝗼𝘃𝗶𝗱𝗶𝗻𝗴 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲-𝗯𝗮𝘀𝗲𝗱 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 with automatic skills assessment. 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: 📄 Supports multiple resume formats (PDF, DOCX, TXT) 📊 Generates automated competency matrices 🤖 Offers smart candidate comparisons 📈 Tracks quantifiable achievements #AzureOpenAI #AI #HR #Recruitment #AzureAI #MSFTAdvocate #Azure #HRTech #FutureOfWork #TalentAcquisition #Agents

I finished an interview yesterday - the profile came through HR  The candidate s resume was so impressive, but the interview left me thoroughly flustered! 😑 But it got me thinking - Gone are the days when Hiring managers or even HR in screening rounds could look at a resume and judge a person on his/her competency for the current role  Chat GPT and other AI tools are so easy to use that Resumes are easily customised according to roles  And its impossible to have so many interviews - Number of interviews needs to come down! 💯 Can there be a business play here - to create a platform or service that leverages AI to gather and verify essential data beyond just resumes. This would help ensure that hiring decisions are made based on a more complete, verified, and accurate profile of a candidate. An AI-Enhanced Verification Platform: Core Idea: A platform that acts as a "candidate credential verification engine" where various metrics are verified automatically to authenticate a candidate's skills, performance, and credibility. This could work similarly to how financial institutions verify your creditworthiness. How It Works: The AI collects and cross-references data from multiple sources: Quarterly/Monthly Awards: The platform can access records from previous employers or even integrate with HRMS systems of companies to automatically pull up performance metrics and awards. Every Org has a R&R - It should be put to some use to determine candidate performance even outside the company ? 🤔 Performance Ratings: The AI tool could pull information regarding annual appraisals or performance ratings Just like credit bureaus track and verify financial information, a centralized AI-driven platform could track and verify a candidate’s work history, performance data, and appraisals, giving orgs an easily accessible and trustworthy history of the candidate's work-life. Recommendations and Endorsements: AI could contact previous managers, HR business partners, and colleagues to gather impartial, genuine recommendations on strengths and weaknesses. The tool can then summarize these endorsements into a digestible format for hiring managers. This can atleast help in identifying the right Training to be given, because yes - Nobody is perfect! 😐 The system could also flag discrepancies between self-reported achievements and verified data. A step further could be - Using psychometric analysis, the platform could assess work styles, collaboration tendencies, and conflict resolution approaches to find the best cultural match. Benefit: This would result in candidates being hired not just for their skills but for their potential to thrive within the organization's culture How many times you hired a candidate who looked like a rockstar on paper but was then responsible for many others from the team leaving ? 🤭 Is this what companies like Darwinbox are aiming to do ? If yes, there seems to be a long journey to get there