Our Case StudiesA smart, AI-driven workforce management solution developed by Softlabs Group. It integrates with existing CCTV infrastructure to automate attendance logging and monitor safety compliance in real-time.
Infrastructure & Manufacturing
AI-Driven Workforce Management
Agile / Iterative
Web-Based Dashboard & Edge AI
A transformative AI initiative by Softlabs Group for one of India’s largest infrastructure conglomerates. The project automated attendance and safety compliance for over 10,000 workers using existing CCTV networks.
The Indian smart manufacturing market is experiencing explosive growth, projected to reach $78 billion by 2033, registering a CAGR of 15.7% from 2026 to 2033. This surge is driving an urgent need to digitize labor-intensive sectors and modernize workforce management
Source: Grand View Research
Softlabs Group seized this gap by engineering a hardware-agnostic AI layer that retrofits existing surveillance infrastructure. This eliminates the need for expensive new sensors while solving the "ghost worker" fraud problem that manual logs and static biometrics fail to address
Source: Qandle HR Software
Traditional biometric systems (fingerprint/card) frequently fail in industrial environments. Reports indicate that "dirty finger" issues-caused by grease, dust, and cuts common in manufacturing-lead to high failure rates in fingerprint recognition, while touch-based systems pose significant hygiene risks
Source: Star Link India
There is a critical shift toward AI-driven safety, with industries increasingly adopting computer vision to detect "unsafe acts" and enforce Personal Protective Equipment (PPE) compliance in real-time, moving from reactive to predictive safety management
Source: 3S Life Safe Akademie
As the AI development partner, we engineered a seamless software layer that connects directly to RTSP-enabled CCTV/IP cameras. This system taps into video feeds to extract high-resolution frames, ensuring clarity even in varying industrial conditions.
The solution leverages Convolutional Neural Networks (CNNs) and Deep Learning to perform two critical tasks simultaneously: facial recognition for attendance and object detection for safety compliance.
We developed an integrated pipeline where detected faces are converted into unique embeddings to verify identity, while the system concurrently scans for required Personal Protective Equipment (PPE). This data is instantly processed and fed into a real-time dashboard, automating payroll logging and triggering immediate alerts for safety breaches.
The system connects to existing RTSP-enabled CCTV cameras installed at facility entry points and work areas, continuously capturing video feeds without new hardware.
The system extracts high-resolution frames from the video stream. Using Computer Vision (CNNs), it detects faces and actions within the frame.
Deep learning networks process each detected face into a unique embedding, which is matched against stored employee data to confirm identity.
Parallel to identification, the AI analyzes the frame for safety compliance, detecting missing items like hard hats, safety vests, or masks.
Once an identity is verified, the system triggers a timestamped attendance entry, effectively "clocking in" the employee without manual action.
If a safety violation (e.g., no helmet) or unauthorized person is detected, real-time alerts are sent to managers. Compliant employees gain automated access.
Processed data is visualized on live dashboards and automatically synced with HR and payroll systems for seamless administrative processing.
Manual systems and spreadsheets led to frequent mistakes and payroll discrepancies
Employees could clock in for one another using badges, inflating work hours.
Manual checks often missed missing PPE like helmets or masks, increasing accident risks.
Managers struggled to see who was present or absent in real-time, slowing decision-making.
Managing manual data drained time and diverted resources from high-value tasks.
Eliminated manual entry by automatically logging timestamps upon face verification.
Used advanced face recognition to ensure precise identity matching, stopping fraud.
Implemented AI checks that verify safety gear (helmets/vests) during the check-in process.
Created live dashboards showing attendance status and compliance alerts instantly.
Linked attendance data directly to HR and payroll systems to automate processing.
Drastically reduced payroll errors and "buddy punching" through precise biometric verification.
lowered accident risks and regulatory fines by monitoring PPE usage in real-time.
Streamlined the check-in process, eliminating queues and reducing administrative workload.
Delivered a system capable of managing large-scale operations across multiple industrial sites.
Uses Deep Learning (Convolutional Neural Networks) to identify employees from video feeds in real-time, eliminating queues and manual clock-ins.
Simultaneously analyzes video frames to detect missing safety gear, such as helmets, masks, and safety vests, ensuring 100% adherence to safety protocols.
Advanced feature extraction creates unique facial embeddings to ensure precise identity matching, effectively stopping "buddy punching" and time fraud.
Processes video data near the source (Edge Computing) to minimize delays, ensuring instant alerts for unauthorized access or safety violations.
Automatically feeds timestamped attendance data and compliance reports directly into existing HR, ERP, and access control systems via APIs.
Workforce attendance and real-time helmet/vest detection
Site safety monitoring and contactless contractor tracking.
Hygiene compliance (mask detection) and staff shift management.
Streamlined driver check-ins and safety vest verification at depots.
Hazardous area access control and strict PPE enforcement.
Python
Node.js
Cloud Platforms
TensorFlow
PyTorch
CNNs
OpenCV
Edge Computing
RTSP/ONVIF
IoT Protocols
Years of Experience
Countries
Clients
Projects
At Softlabs Group, we take pride in solving complex business challenges with innovative and reliable solutions. Our case studies showcase how we’ve empowered clients across industries with tailored software that delivers measurable results and drives success.
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Built a compliant and user-friendly platform to streamline the borrowing process while adhering to legal standards.
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Delivered a robust platform enabling real-time data management, regulatory compliance, and efficient operations.
Enhanced operational efficiency, ensuring accurate data handling and seamless compliance adherence.
FAQs
We start with a thorough understanding of the business challenge and data availability. Based on this, we select appropriate AI techniques—be it machine learning, deep learning, NLP, or computer vision. We also consider factors like scalability, accuracy and model explainability.
We do both. For complex, domain-specific challenges, we build models from scratch. For faster deployments, we adapt pre-trained models using transfer learning or fine-tuning, depending on your data and goals.
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Project duration varies based on complexity. A basic AI prototype can be developed in 4-6 weeks. A production-ready system, including model training, integration, and testing, typically takes 2–3 months.
We use industry-standard evaluation metrics (e.g., precision, recall, F1-score) and iterative validation to ensure high accuracy. We also run stress tests in real-world environments and continuously improve models based on live data.
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