TechCorp India was processing payroll for 1,200+ employees using spreadsheets and legacy tools. The process took 3 full days, was prone to errors, and compliance checks were done manually — risking penalties.

• 72-hour payroll cycle blocking finance operations
• PF/ESI/TDS errors causing statutory notice risk
• No real-time attendance or leave visibility
• Zero self-service for employees — HR overwhelmed

We built a full-cycle HRMS with an AI payroll engine that auto-calculates taxes, flags anomalies, and generates compliance reports. The system integrates attendance hardware, leave workflows, and an employee self-service portal.

• AI payroll engine with automatic tax computation
• Biometric + mobile attendance with geo-fencing
• Smart leave management with policy enforcement
• Real-time compliance dashboard + auto-filing

Apex Manufacturing was generating 500+ invoices daily across multiple GSTINs with varied tax structures. Manual processes were causing ITC mismatches and compliance delays.

• Multiple GSTIN management across 4 states
• Manual ITC reconciliation taking 2 days/month
• E-invoice mandate causing integration chaos
• GSTR-1 filing errors due to data inconsistencies

A fully automated GST platform with e-invoice API integration, smart ITC reconciliation engine, and multi-GSTIN management. Auto-generates GSTR-1 and GSTR-3B data.

• Automated e-invoice via NIC IRP integration
• Smart ITC matching with supplier invoice sync
• Auto GSTR-1/3B report generation
• Real-time tax liability dashboard

FinEdge's HR and finance teams were handling 300+ daily queries about policies, payroll, and compliance. Response SLAs were being missed and employees were frustrated.

• 300+ daily queries overwhelming HR team
• Inconsistent answers from different team members
• 50,000-document knowledge base unsearchable
• Zero 24/7 support availability

We built a production RAG pipeline indexing 50,000 documents with semantic chunking, custom re-ranking, and source citation. The LLM is fine-tuned on enterprise HR and finance context.

• Vector database with 50K+ indexed documents
• Multi-hop reasoning for complex queries
• Source-cited responses for compliance
• Anomaly detection integration with HRMS

RetailMax had data across 12 systems but no unified view. Reports took 3 days to compile and were outdated by the time executives saw them. Decisions were reactive, not proactive.

• 12 siloed data sources with no integration
• 3-day reporting lag for business decisions
• No anomaly detection or early warning
• Zero predictive capability for planning

We built a streaming data pipeline using Kafka + Spark, a lakehouse on AWS S3/Athena, and a React BI dashboard with ML-powered forecasting and anomaly detection.

• Kafka streaming pipeline for all 12 data sources
• Spark for real-time aggregation and enrichment
• ML forecasting with 94% accuracy
• Automated anomaly alerts to Slack/email