SUCESS STORY
Agentic AI – Customer Service Classification
Client and engagement overview
The client is a prominent fintech company in India, known for its digital payment solutions. It is a subsidiary of one of the leading omnichannel payments and banking platforms.
The client specializes in comprehensive payment solutions, including mPOS and smart POS terminals, with a focus on financial inclusion, making digital payments accessible to small businesses and rural consumers. Its integration with the payment and banking platform enhances its omnichannel capabilities, allowing seamless management of both online and offline payments.
Challenges
High ticket volume
Agents are often overwhelmed by a large number of tickets, which causes delays in classification and resolution.
Complex categorization
Manual classification is complex and error-prone due to the wide variety of ticket categories. Misclassification can result in incorrect routing or delays.
Operational inefficiency
Having agents handle each ticket individually lowers efficiency and creates backlogs, especially when dealing with high ticket volumes.
Time-consuming data extraction
Manually extracting details from each ticket is time-intensive and reduces the agent's ability to focus on resolving issues. It also increases the risk of human error.
Inconsistent data entry
Manually verifying and entering required details increases the risk of missing critical information, leading to rework or escalations.
Our solution
Mindfields analyzed the existing process and advised an Agentic AI approach by integrating Intelligent Automation (IA) and Generative AI. This comprehensive analysis involved a detailed examination of each process step to ensure optimization and cost-effectiveness. The team then developed a compelling business case, incorporating key metrics such as transaction volumes, average handling time, and SLAs obtained from the client.
The IA maturity rating, according to our framework, confirmed that this process was ideally suited for automation with the combined power of IA and GenAI. The assessment and the showcase concluded that the process was viable for automation and demonstrated a feasible business case.
Mindfields employed an IA + Gen AI approach to automate the client's Customer Request Management flow, enhancing the efficiency and accuracy of processing customer inquiries. IA automatically retrieved and parsed emails, while Gen AI classified the content and extracted relevant data points such as customer details and request specifics. The extracted data was then validated and enriched using IA before updating the internal system.
Benefits
The automation of the process delivers the following benefits:
Faster processing
Automation processes large volumes of tickets much faster than manual agents, reducing ticket backlog and improving response times.
Higher accuracy
GenAI models automatically classify tickets into the correct categories with high precision, minimizing misclassification and speeding up routing to the right teams.
Efficient data extraction
Quickly and accurately extract necessary details from tickets, reducing errors and allowing agents to focus on more complex tasks.
Scalability
Automated systems easily scale to handle growing volumes without affecting performance, making them ideal for expanding businesses.
Cost savings
Intelligent Automation reduces the need for manual intervention, lowering operational costs and freeing up human resources for more strategic work.
Improved operational efficiency
By automating repetitive tasks, agents focus on high-value activities like complex ticket resolution, improving resource use and throughput.
Better SLA compliance
Intelligent Automation ensures tickets are processed within required timeframes, helping meet service-level agreements and boosting customer satisfaction.
