Automation of Customer Support and Document Processing for an Education Consultancy

Enabled real-time health analytics, processing data queries in minutes, dramatically improving operational efficiency and patient care responsiveness.

Technology

OCR Technology
Finetuned PaddleOCR model and deployed to AWS. Utilized advanced OCR tools to convert images and scanned documents into editable and searchable text.
Natural Language Processing (NLP)
Employed NLP techniques for entity extraction and to power the LLM-based chatbot, enhancing its ability to understand and respond accurately to complex queries.
Cloud Services
Hosted on secure cloud platforms to ensure scalability and reliability of the document processing and chatbot services.
CRM Integration
Custom APIs were developed to ensure seamless data flow between the automated systems and the existing CRM software.

OCR and Entity Extraction Pipeline	Description:This system was developed to automatically process and manage various types of educational documents, such as application forms, transcripts, and letters of recommendation. The OCR technology extracted text from scanned documents, while the entity extraction module identified and classified key information like names, dates, and course details for further processing.
LLM-Based Chatbot for Query Handling	Description:A chatbot powered by a large language model was integrated into the consultancy’s website and support channels. This chatbot was trained to understand and respond to a wide range of student queries, from course details to application procedures. It could also initiate support tickets when human intervention was needed, ensuring a seamless escalation process.
Automated Document Management	Description:The automated system categorized, stored, and retrieved documents efficiently, reducing the time staff spent on manual document handling. It also ensured that all student records were securely stored and easily accessible for future reference.
Integration with Existing CRM	Description:The entire system was integrated with the consultancy's existing Customer Relationship Management (CRM) software. This integration facilitated a unified view of each student's interactions and documents, enhancing the consultancy's ability to provide personalized support.

Impact and Results

The time required to process and manage educational documents was reduced by 60%, thanks to the OCR and entity extraction pipeline.
The LLM-based chatbot handled up to 70% of routine queries, freeing human agents to focus on more complex and personalized student interactions.
By automating routine tasks and document processing, the consultancy reduced its operational costs by 30%.
The accuracy of document processing improved to over 95%, and secure document management ensured compliance with data protection regulations.
Students and applicants experienced faster responses and more accurate information, leading to higher satisfaction rates and improved service quality.

Conclusion

The consultancy's adoption of an automated customer support and document processing solution transformed its operations and significantly improved its service delivery. By integrating OCR, entity extraction, and an LLM-based chatbot, the consultancy not only streamlined its internal processes but also enhanced the overall experience for students and staff. This strategic move positioned the consultancy as a leader in leveraging technology to support educational aspirations efficiently and effectively.