Revelio Labs

Advanced workforce analytics solutions that help organizations unlock insights from their people data to drive better talent decisions.

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Technologies Used

React
Next.js
TypeScript
TailwindCSS
Node.js
Python
FastAPI
PostgreSQL
Snowflake
Redis
Airflow
AWS
Docker
Tableau
Looker
Recharts
Machine Learning (NLP, Predictive Models)

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Introduction

Revelio Labs delivers advanced workforce analytics solutions that transform organizational people data into actionable insights. By combining natural language processing, predictive modeling, and large-scale data pipelines, the platform empowers HR teams, executives, and analysts to make smarter decisions on talent management, workforce planning, and organizational strategy.

Revelio Labs bridges the gap between raw HR data and strategic business outcomes, enabling organizations to anticipate workforce trends, benchmark against industry peers, and optimize talent strategies with confidence.

Key Features

  • Organizational Insights: Centralized dashboards that provide visibility into workforce composition, hiring trends, attrition risk, and diversity metrics.
  • Predictive Analytics: Machine learning models forecast workforce changes, such as turnover likelihood, internal mobility, and role demand.
  • Industry Benchmarking: Compare organizational metrics against industry benchmarks to identify strengths, gaps, and competitive positioning.
  • Data Integration: Secure connectors for HRIS, ATS, and payroll systems, enabling seamless ingestion of people data from multiple sources.
  • Custom Reporting & Visualization: Rich reports and dashboards powered by Tableau/Looker, providing interactive drill-downs for stakeholders across HR and leadership.

Technical Insights

  • Frontend: Built with React, Next.js, and TypeScript, delivering a performant and accessible interface. TailwindCSS provides a clean and responsive design system optimized for analytics dashboards.
  • Backend & APIs: A Node.js orchestration layer works alongside Python (FastAPI) services that handle machine learning models, natural language processing, and analytics pipelines.
  • Data Pipeline & Storage: Workforce data is ingested through Airflow-based pipelines and processed into Snowflake for large-scale analytics. PostgreSQL supports transactional data and system metadata, while Redis handles caching for frequent queries.
  • Machine Learning & Analytics: ML models leverage NLP for job/title normalization, clustering for workforce segmentation, and predictive models for attrition and mobility analysis.
  • Visualization & Reporting: Interactive dashboards are built with Tableau and Looker, enabling customizable reporting for executives and analysts.
  • Cloud Infrastructure: Hosted on AWS, leveraging EC2 for compute, S3 for data storage, and RDS for database management. Dockerized services ensure reproducibility and scalability.

Challenges and Solutions

  • Heterogeneous Data Sources: HR data often comes from disparate systems with inconsistent formats. We implemented ETL pipelines with Airflow to normalize, clean, and unify workforce data for analysis.
  • Scalability of Analytics: Processing millions of employee records across industries required robust infrastructure. We solved this with Snowflake’s elastic compute and AWS scaling policies to handle peak demand efficiently.
  • Data Privacy & Compliance: Ensuring secure handling of sensitive workforce data was critical. We enforced data anonymization, encryption at rest/in transit, and strict access controls aligned with compliance frameworks like GDPR.
  • Complexity of Predictive Modeling: Predicting attrition and workforce changes involves multiple variables. We addressed this by combining NLP for job/title normalization with ensemble ML models validated against historical datasets.
  • User Adoption & Accessibility: Complex analytics risked overwhelming non-technical users. To solve this, we built intuitive dashboards and pre-configured templates, making insights accessible to HR leaders without deep technical expertise.