AI-Driven IT Support & Ticket Automation Engine
The client struggled with inefficiencies in its IT support system, often facing incomplete ticket processing and communication bottlenecks. Back-and-forth clarifications delayed resolution, and the lack of a unified knowledge base meant that even simple issues consumed valuable IT staff time.
A leading innovator in the agricultural technology sector, responsible for maintaining complex IT infrastructure across widespread operational bases. The organization faced scaling challenges with its internal support systems as its workforce and technological footprint expanded.
AI-Guided Ticket Creation
Leveraging LLaMA models to assist users in drafting complete tickets via a chat interface, ensuring all necessary diagnostic details are captured upfront.
RAG Knowledge Context
Integration of Retrieval-Augmented Generation (RAG) to pull verified solution steps from TopDesk, Confluence, and OneNote, suggesting immediate fixes based on historical data.
Automated Triage & Resolution
An intelligent routing engine that auto-resolves simple queries and categorizes complex incidents by urgency and topic, dispatching them to the correct specialist.
Inefficient Workflows
Incomplete ticket data led to endless back-and-forth communication, significantly delaying problem resolution.
Knowledge Silos
Critical troubleshooting information was scattered across disparate platforms (Confluence, OneNote, legacy logs), making manual search time-consuming.
Scaling Support
The existing human-centric support model could not keep pace with the growing volume of tickets from a rapidly expanding workforce.
We implemented an end-to-end AI-driven ticketing ecosystem. Powered by LLaMA and RAG architectures, the system guides users to provide perfect ticket data, auto-resolves routine queries using existing documentation, and intelligently routes complex issues. A feedback loop ensures the model continuously learns from successful resolutions.
