Design a secure facial recognition system for real-time visitor authentication and tracking in high-profile government buildings like Sachivalay. The solution aims to enhance security, streamline visitor management, and achieve 99% accuracy in identification. It must handle high visitor volumes securely while complying with data protection laws and delivering real-time analytics for administrative and security teams.

Develop a bi-lingual OCR system to accurately extract text in English and Gujarati from scanned documents and images, supporting diverse fonts and formats. The solution targets government departments, businesses, and academic institutions, aiming to digitize the process, reduce manual transcription efforts, and improve data accessibility. It must achieve >95% accuracy, handle low-quality scans and handwritten text, and comply with regional data privacy standards.

Develop an AI-driven solution to automatically identify, classify, and recognize document types like invoices, ID cards, and certificates while extracting key metadata fields. Targeting government agencies, banks, and businesses, the system aims to improve workflow efficiency with >90% accuracy. It must handle noisy, unstructured documents securely and comply with data privacy standards, significantly reducing manual processing efforts.

The use case focuses on leveraging AI to identify fake calls made to Emergency Response Support System (ERSS) 112. By analyzing call recordings and extracting Call Data Records (CDR), AI-powered speech recognition detects specific keywords, patterns, or anomalies in spoken words that may indicate a hoax. The system flags such calls in real-time and alerts authorities to prioritize genuine emergencies, improving response efficiency and reducing operational strain on emergency services.

The use case focus on a solution that integrates CCTV surveillance with Facial Recognition Systems (FRS) to enhance the identification of inmates, staff, and visitors using body-worn cameras. This system aims to improve security by enabling real-time monitoring and reducing the risk of unauthorized access. By leveraging FRS, the solution would address challenges like manual verification, delayed threat detection, and operational inefficiencies, creating a safer and more controlled environment.

Surgery for head and neck cancers poses significant challenges due to the proximity of critical structures. While clinical examination and radiological imaging provide valuable insights into the surgical landscape, unexpected complexities often arise during the procedure. By integrating AI-powered analysis of pre-operative imaging with immersive simulations of intra-operative conditions, surgeons can visualize surgical planes in relation to adjacent structures with enhanced accuracy. This approach not only improves pre-operative preparation but also reduces intra-operative uncertainties, leading to better outcomes. Additionally, this solution enables a training platform for surgical trainees, enabling them to practice and navigate challenging surgical scenarios in a controlled, risk-free environment.

Develop a GenAI-enabled chatbot to assist employees with a wide range of tasks, from content writing, document summarization, and email drafting to HR queries and customer service support. Designed for versatility, the chatbot facilitates learning and development, compliance, team collaboration, and automation of repetitive tasks. It will provide personalized assistance, streamline workflows, and enhance productivity while allowing for seamless integration of additional use cases in the future.

Develop an AI-driven predictive maintenance solution for EV charge point operators to transition from reactive to proactive maintenance practices. By analyzing real-time data from OCPP notifications, historical maintenance logs, and environmental factors, the system will predict potential failures, optimize maintenance schedules, and assign priority levels based on customer impact. It will include an AI-based alert system, actionable insights for technicians, and a centralized dashboard for monitoring charger health and maintenance efficiency, reducing downtime and improving operational efficiency.

Transforming legacy medical records into digital assets is often hampered by their non-digital format, complexity, and sensitive nature. Traditional manual processes are slow, error-prone, and resource-intensive. This can be overcome by an AI-led solution that enables automated extraction, classification, and digitization of medical documents with unparalleled accuracy and speed. The solution needs to have stringent privacy and security frameworks, ensuring the compliance with regulations, safeguarding patient confidentiality. The solution enhances operational efficiency, reduces costs, and ensures the long-term accessibility and integrity of critical medical data.