A high-volume customer service team faced lengthy resolution times for complex queries. Agents relied on a dedicated Slack channel where tenured agents provided assistance. This manual process resulted in an average resolution time of 5 minutes, negatively impacting customer satisfaction (CSAT).
Our services encompass a wide range of applications, including predictive analytics, natural language processing, image and speech recognition, and automation.
By harnessing the power of machine learning, we help businesses gain deeper insights from their data, improve decision-making processes, and enhance operational efficiency. Our team of experts works closely with clients to understand their specific challenges and develop customized machine learning solutions that deliver measurable results.
This data contained the query text and corresponding response from a tenured agent.
A structured collection of pre-written answers to frequently asked questions (FAQs) and troubleshooting steps.
Techniques like topic modeling or keyword extraction were used to group similar queries together.
LLM-based training or supervised machine learning models were used to analyze the combined dataset (Slack data + knowledge base) and identify patterns for generating relevant responses.
The bot uses a combination of pre-programmed responses based on keywords and the ability to generate new responses based on its training data. This involves techniques like rule-based chatbots or more advanced conversational AI models.
The AI Assistant Bot was integrated seamlessly into the existing Slack channel, minimizing disruption for agents.
A beta testing phase ensured the bot's accuracy by comparing its responses to a human expert's solutions.
The AI Assistant Bot significantly improved customer service efficiency and satisfaction
This translates to faster resolution times for complex queries.
Faster resolutions and accurate information lead to happier customers.
A wholesale distributor sought to expand their business by offering both B2B and B2C solutions through an online retail platform. The distributor faced difficulties in managing complex inventory across multiple warehouses, catering to diverse customer needs, and providing seamless user experiences for both vendors and buyers.
Dev Pandas implemented a comprehensive online platform, including web and mobile applications for vendors, business buyers, and consumers.
The solution involved the following key components
Developed user-friendly web and mobile applications for vendors, business buyers, and consumers.
Implemented a robust inventory management system to track stock across multiple warehouses efficiently.
Provided a dedicated portal for vendors to manage products, inventory, and orders.
Designed intuitive interfaces for business buyers and consumers to browse products, place orders, and track deliveries.
AWS for scalable and secure hosting
Custom algorithms for real-time stock updates and multi-warehouse coordination.
React (web), Swift (iOS), Kotlin (Android)
Node.js, Express.js, MongoDB
Ensured smooth integration of the platform with existing systems and third-party services.
Conducted extensive beta testing to ensure the platform’s performance, usability, and reliability.
The B2B and B2C solution significantly improved the distributor's operations and customer satisfaction
The online platform facilitated easier and quicker transactions, boosting sales.
Multi-warehouse management reduced stock discrepancies and optimized inventory levels.
Automated processes and real-time updates accelerated order fulfillment.
The user-friendly interface and seamless experience increased customer loyalty and satisfaction
