Artificial Intelligence

Enhancing Worker Safety with Conversational AI and GenAI-based Automation

Deployed AI-powered conversational and predictive tools to enhance factory worker safety, automate workflows, and improve operational efficiency across the organization.

Problem Statement
  • A US-based safety solutions provider sought to improve the way factory workers interact with operational systems and safety workflows.
  • The existing system relied on rigid, rule-based workflows, lacked real-time adaptability, and offered limited analytics capabilities.
  • They needed a smarter, more intuitive solution that could support voice and text interactions, improve automation, and deliver actionable insights through predictive analytics.
Solution Design
  • Implemented Conversational AI for workers using speech and text interfaces powered by Natural Language Processing.
  • Replaced legacy rule-based workflows with an AI-driven inference engine for dynamic decision-making.
  • Integrated Action AI to support and enhance existing workflow automation systems.
  • Deployed Predictive AI models using Generative AI to replace traditional analytics, enabling smarter forecasting and proactive safety measures.
Business Impact
  • Successfully validated the solution through multiple Proof of Concepts and began full-scale rollout.
  • Demonstrated marked improvement in efficiency and user satisfaction compared to legacy solutions.
  • High adoption rate observed among users during the pilot phase, indicating strong alignment with field needs.
  • Established a scalable, AI-powered foundation for future innovation in worker safety and automation.

Automating Service Parts Cataloging with GenAI-Powered Classification

Implemented a GenAI-based auto-classification system for Stellantis Mopar to streamline cataloging, reduce costs by 63%, and increase tagging accuracy and efficiency.

Problem Statement
  • Client division was spending over $2.1 million annually to manually classify and catalog service parts into searchable groups and subcategories.
  • The manual effort was time-consuming, error-prone, and expensive.
  • The need for a scalable, intelligent solution to reduce operational costs while ensuring high classification accuracy became critical for efficiency and sustainability.
Solution Design
  • Leveraged Gen AI Studio to automate the part cataloging process for Mopar.
  • Deployed an Automated Classification Engine that extracted data from existing databases and applied contextual logic to classify new parts using historical knowledge.
  • Integrated a Knowledge Assistant that utilized knowledge graphs to build new catalogs with minimal human input.
Business Impact
  • Reduced costs by 63% through automation and workflow optimization.
  • Reduced overall manual effort by 70% through automation and AI-driven decision-making
  • Achieved high accuracy across all catalogs with consistent precision in auto-classified entries, reducing human errors and boosting reliability.

Enhancing Student Support with AI Chatbot for Overseas Education Portals

Deployed an AI-powered chatbot to provide 24/7 personalized assistance, reducing support load by 40% and improving user satisfaction and engagement significantly.

Problem Statement
  • The client’s platform, offering overseas education and work opportunities, was overwhelmed with content, making it difficult for users to find relevant information.
  • Students frequently had specific queries about tuition, visa processes, and universities, but lacked immediate support.
  • The absence of 24/7 assistance led to delayed responses and missed opportunities, especially for international users in different time zones.
Solution Design
  • Developed an Advanced Chatbot using Microsoft Virtual Agent to provide 24/7 support.
  • Integrated Azure Cognitive Services to interpret and respond to user queries using natural language processing.
  • Utilized QnAMaker to enrich the chatbot with country-specific education content and FAQs
  • Enabled Real-Time Support to deliver immediate answers, improving responsiveness.
  • Added Power Automate as a fallback mechanism to ensure smooth query handling even in complex cases.
Business Impact
  • 30% boost in satisfaction rates according to user surveys.
  • 25% increase in web engagement with more time spent exploring educational content.
  • 40% drop in manual support queries, reducing operational load.
  • 50% surge in positive feedback, highlighting user approval for the AI-driven support experience.