AI-Powered Recruitment & HR Analytics Platform
End-to-end HR platform with AI-driven candidate matching, automated screening, and predictive workforce analytics for scaling companies
Overview
TalentPulse is an HR SaaS company serving mid-market and enterprise companies in India. We built their next-generation platform that uses AI to transform recruitment—from sourcing and screening to interview scheduling and offer management—while providing people analytics that help HR leaders make data-driven workforce decisions.
The Problem
Recruitment is broken: recruiters spend 80% of time on administrative tasks instead of candidate relationships. Companies lose great candidates to slow processes. New hires quit within 6 months because of poor job-person fit. HR leaders fly blind without data on what makes employees successful or likely to leave.
Key Challenges
Recruiter Overload
Each requisition received 200+ applications. Recruiters couldn't screen all resumes thoroughly. Great candidates got lost in the pile. Manual scheduling coordination took hours per interview. Recruiters were glorified administrators instead of talent advisors.
Poor Candidate-Job Matching
Keyword matching missed qualified candidates with non-traditional backgrounds. Hard to assess cultural fit before hiring. Interview feedback was unstructured and inconsistent. Bad hires were expensive—3-6 months salary to replace.
Slow Hiring Process
Average time-to-hire was 45+ days. Candidates dropped out due to slow feedback. Multiple interview rounds without coordination. Offer letters created manually for each hire.
Workforce Visibility Gap
No data on what makes employees successful in each role. Attrition predictions came after resignation. Succession planning was gut-feel based. Diversity metrics tracked but not actionable.
How We Built It
Smart Sourcing & Screening
Built AI resume parser extracting structured data from any format. Developed semantic matching between job descriptions and candidate profiles. Implemented automated skill assessments and video screening. Created candidate scoring with explainable criteria.
Interview Intelligence
Built automated scheduling handling multi-party calendar coordination. Created structured interview guides with role-specific questions. Implemented interview feedback capture and bias detection. Developed AI-assisted interview summaries from recordings.
Hiring Workflow Automation
Automated offer letter generation with configurable templates. Built approval workflows for compensation decisions. Integrated background verification and document collection. Created candidate portal for self-service onboarding.
Workforce Analytics
Developed employee success profiles by role and team. Built attrition prediction models with 90-day early warning. Created skills gap analysis for learning recommendations. Implemented diversity and inclusion dashboards with actionable insights.
Solution Highlights
AI Candidate Matching
Semantic understanding of skills and experience, not just keywords. Matches candidates to roles based on success patterns of top performers. Surfaces hidden gems from non-traditional backgrounds.
Automated Screening
AI screens resumes, sends skill assessments, and conducts initial video interviews. Recruiters focus on qualified candidates only. Response time drops from days to hours.
Interview Copilot
Automated scheduling across all participants. Structured interview guides for consistency. AI captures feedback and flags potential bias. Interview summaries generated from recordings.
Predictive HR Analytics
Early warning for attrition risk. Success profiles for each role guide hiring and development. Skills gap analysis informs L&D investments. Real-time diversity metrics.
Technical Deep Dive
The candidate matching system uses a two-tower neural network architecture trained on historical hiring data, learning embeddings for both job descriptions and candidate profiles. Similarity is computed in this shared embedding space, enabling semantic matching beyond keyword overlap. The resume parser uses a combination of NER models and LLMs to extract structured data from varied formats including PDFs with complex layouts. Attrition prediction uses survival analysis models with features from engagement patterns, compensation data, manager interactions, and external market signals. Interview summarization uses Whisper for transcription and GPT-4 for extractive summarization with bias detection prompts.
AI Capabilities
Resume Parsing
Extracting structured data from any resume format
Semantic Job Matching
Neural networks matching candidates to roles beyond keywords
Skill Assessment
Automated technical and aptitude testing with adaptive difficulty
Interview Analysis
Transcription, summarization, and bias detection from interviews
Attrition Prediction
90-day early warning for employees likely to leave
Success Profiling
Learning what makes top performers in each role
Technology Stack
AI/ML
Backend
Frontend
Integrations
Analytics
Infrastructure
Results & Outcomes
-70%
Time-to-hire reduction
From 45+ days to under 14 days average
-50%
Early attrition
Better matching reduces 90-day turnover
80%
Recruiter time saved
Automation handles administrative tasks
95%
Scheduling automation
AI handles multi-party coordination
90 days
Attrition early warning
Predicting turnover risk in advance
50K+
Hires processed
Platform volume in first year
“TalentPulse transformed our recruiting team from resume screeners to true talent advisors. The AI handles the admin work so we can focus on building relationships with candidates.”
VP of People
TalentPulse Customer
Services Used for the Client Product
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