Business Need
A global engineering enterprise was facing a sharp rise in internal support tickets related to product data, service manuals, and maintenance documentation. Engineering teams were spending excessive time manually searching across data sources from structured product databases and unstructured PDF documents to locate accurate information.
The lack of a unified, intelligent search experience resulted in delayed access to critical product specifications, troubleshooting steps, and service procedures. This manual, fragmented process not only slowed response times and impacted decision-making but also diverted skilled engineering resources away from high-value innovation and problem-solving activities.
The organization needed a secure, scalable solution that could rapidly retrieve precise, context-aware engineering information from multiple data sources, without compromising data security or usability.
Our Solution
TechVedika designed and delivered a secure, enterprise-grade Gen AI chatbot powered by a Retrieval-Augmented Generation (RAG) architecture. The solution unified structured data from SQL database and unstructured engineering documents into a single, intelligent knowledge layer, enabling engineers and support teams to access the right information instantly through natural, conversational queries.
Built with a strong focus on security, usability, and performance, the chatbot transformed how internal teams discover and consume engineering knowledge. By replacing manual search with AI-driven insights, the solution accelerated support workflows, improved accuracy, and significantly enhanced productivity.
Key Features
- Secure, Enterprise-Ready Chatbot: Developed using React with an Azure Copilot-inspired UI and integrated with OKTA for robust authentication and access control, ensuring sensitive engineering data remains protected.
- Smart Product Search: Enables intuitive searches using product numbers, labels, and functions, dramatically reducing time spent locating relevant information.
- Intelligent Information Retrieval: Leverages a RAG-based approach over engineering PDFs to deliver precise, context-aware responses, complete with source citations for traceability and trust.
- Adaptive, Context-Aware Conversations: Supports seamless topic transitions, product group changes, and conversation restarts, delivering a natural and efficient user experience.
- Feedback and Conversation History: Captures user feedback and maintains chat histories to support continuous improvement, auditing, and usage insights.
- Integrated Ticketing Workflow: Allows users to generate support tickets directly from the chatbot, with automated extraction of relevant chat context as ticket inputs.
Business Impact
- Over 85% accuracy, significantly reducing dependency on manual support and lowering ticket volumes.
- Substantial reduction in manual effort required to search across multiple systems and documents.
- Improved engineer productivity, enabling teams to focus on core engineering and innovation rather than information retrieval.
- Laid the foundation for scalable, reusable AI models for future enhancements.
Conclusion
This solution showcases the transformative potential of GenAI in unlocking enterprise engineering knowledge. By combining secure architecture, intelligent document understanding, and advanced retrieval techniques, TechVedika delivered a high-impact platform that improved support efficiency, accelerated decision-making, and drove measurable productivity gains.
The success of this engagement has laid a strong foundation for future phases, including deeper analytics, advanced AI capabilities, and broader enterprise adoption, further amplifying business value and reinforcing TechVedika’s leadership in delivering practical, enterprise-ready Gen AI solutions.