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.
Manual safety checks could miss PPE violations, restricted-area entry and unsafe worker movement.
Built an AI vision system using existing CCTV to detect violations and send instant alerts with visual evidence.
24/7 monitoring, faster intervention, reduced safety risks and centralised compliance visibility.
Manual checks could not reliably confirm whether quarry trucks were safely covered before leaving the site.
Developed an edge AI system that detects truck covers and connects with boom barriers for automated exit control.
95%+ detection accuracy, automated monitoring, lower compliance risk and deployment in under two weeks.
Manual monitoring could overlook vibration exposure, worker fatigue and workplace accidents.
Developed an AI-enabled smartwatch that tracks vibration, worker vitals, falls and exposure levels in real time.
Earlier safety warnings, stronger compliance, fewer health disruptions and better exposure visibility.
Golfers lacked accurate, real-time feedback to understand and improve their putting technique.
Developed a compact AI device with motion tracking, stroke analysis and instant feedback through a mobile app.
A functional, market-ready product with personalised training insights and positive early-user feedback.
Manual inventory checks caused errors, delayed stock updates and limited visibility across warehouse operations.
Built a computer vision system that monitors stock movement and provides real-time inventory data through a central dashboard.
98%+ tracking accuracy, live stock visibility, reduced manual work and improved operational efficiency.
Information desks faced long queues, repetitive enquiries, language barriers and outdated passenger information.
Built a life-sized multilingual AI concierge using governed RAG and verified airport information.
Zero waiting for general enquiries, 95% accurate resolution, improved passenger experience and lower support workload.
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.
AI projects require structured or unstructured data relevant to the problem-this could include sensor data, images, audio, videos, text logs, or tabular datasets. We assist with data assessment, cleaning, and augmentation if needed.
Yes, we can deploy AI on both. For real-time or offline use cases, we optimize models to run on edge devices like mobile phones, embedded boards, or wearables. We also offer cloud-based options for heavy computing or centralized access.
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.
Yes, integration is a core part of our delivery. We build flexible APIs, SDKs, or direct software connectors to seamlessly plug AI into your existing platforms or devices.
Absolutely. We offer post-deployment support, including model monitoring, performance tuning and retraining if new data trends emerge. Our goal is to keep your AI solution relevant and effective over time.
That’s something we help evaluate. If rule-based systems or automation can solve the problem, we say so. But if the problem involves patterns, predictions, or personalization at scale, AI is often the best fit.
We follow industry best practices for data security and adhere to regulations like GDPR and HIPAA where applicable. We also offer anonymization and on-premises deployments for sensitive data scenarios.
