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RecruitAId

Revolutionizing Recruitment Lifecycle Management with AI-Powered Insights and Dynamic Assessments.

Related case studies

AI-Powered Customer Onboarding Agent for EdTech Mentorship Platform

Data Science

An education technology company providing mentorship programs for universities and corporations faced a 6–8 week manual onboarding cycle that relied on unscalable tribal knowledge and synchronous CSM labor for each partner configuration. A conversational AI onboarding agent was deployed using a LangGraph-based multi-agent system on AWS Bedrock with RAG-powered recommendations and human-in-the-loop approval, reducing infrastructure costs to approximately $5.32 per new partner onboarding while enabling partners to self-serve 68+ program design decisions asynchronously.

AI Automation for Industrial Aftermarket Maintenance

Data Science

A heavy industrial power generation client faced an unsustainable maintenance documentation challenge: manually extracting preventive maintenance tasks from thousands of pages of fragmented OEM manuals, vendor handbooks, and site procedures required up to 480 hours per 12-asset plant and systematically missed hidden safety-critical tasks embedded in appendices and commissioning guides. By deploying a Google Gemini-powered semantic extraction platform with parallel processing, cross-document knowledge graph unification, and a RYGB confidence validation framework, the solution achieved 160x faster processing (40 hours to 15 minutes per asset), a 469% expansion in discovered maintenance activities over human baselines, and a 94–96% reduction in extraction costs—transforming static documentation into living operational intelligence at enterprise scale.

Clinical Application for Tracking and Managing Autoimmune Diseases

Data Science

A digital health startup is building the intelligence layer for patients living with MS, RA, and Lupus — an alpha-stage platform focused on the data collection phase. It integrates Apple Watch and Fitbit biometrics with structured daily check-ins and flare reports, all stored in database, and enriches onboarding with Claude AI–powered community insights derived from real patient. The system delivers a production-ready REST API with 30+ endpoints covering onboarding, daily tracking, flare reporting, wearable ingestion, drift detection, home dashboard, and account management. It also includes personalized z-score–based anomaly detection, statistically rigorous community intelligence built from 50+ patient cohorts, and foundational infrastructure for proactive flare prediction in future releases.

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