Teller Assistance

Overview

The AI-Powered Teller Assistance Agent is a multi agent system designed to revolutionize in branch banking operations by enhancing teller customer interactions with real time, context aware information retrieval. Built on a flexible, modular framework using Lyzr AI, this intelligent agent listens to live conversations between tellers and customers, proactively surfacing relevant knowledge base articles, policy documents, FAQs, and product details. Unlike traditional teller support systems that rely on manual lookups and static information, this agent offers a dynamic, transparent, and auditable solution that seamlessly integrates with your bank’s internal systems and knowledge repositories, resulting in faster, more accurate service and improved customer satisfaction.

Problem Statement

Traditional teller operations are frequently hampered by inefficient manual searches and fragmented access to critical information, which results in delayed responses as tellers must navigate multiple systems to locate policies, product details, or procedural guidelines, causing prolonged wait times for customers. The variability in responses across branches due to human error and limited access to a centralized repository leads to compliance risks and customer dissatisfaction. New staff require extensive training to familiarize themselves with scattered resources, and even experienced tellers struggle with rapidly changing information, resulting in high training and operational costs. The reliance on manual lookup processes not only slows down service delivery but also increases the risk of operational bottlenecks during peak times. Financial institutions need a modular, AI-native teller support solution that can intelligently analyze live conversations, dynamically retrieve pertinent information, and deliver real-time, consistent answers to customers.

Agent Blueprint(Excalidraw diagram)

Agent Blueprint Flow Explanation

When a teller initiates a customer interaction, an orchestrated sequence of intelligent agents is activated through Lyzr AI to perform real-time conversation capture, query analysis, and context-driven information retrieval. The Conversation Capture Agent begins by listening to live teller-customer interactions via audio or text, utilizing advanced speech-to-text algorithms and natural language processing to generate accurate transcripts that capture conversation nuances and context.

The Query Analysis Agent then processes the conversation transcript to identify keywords, intents, and potential queries using NLP techniques and semantic analysis to extract actionable insights and determine underlying customer needs. Simultaneously, the Knowledge Base Retrieval Agent searches the bank’s internal document repositories for relevant information such as policies, FAQs, and product details, leveraging vector search and metadata tagging to match query context with the most pertinent documents and data sources.

The Contextual Ranking Agent evaluates and prioritizes retrieved search results based on relevance to the ongoing conversation, implementing machine learning models to assess context, teller preferences, and historical usage patterns to ensure top results align with the query. The Display & UI Agent presents the conversation transcript alongside real-time search results and relevant document links in an intuitive dual-pane interface, featuring live conversation on the left and dynamic search results on the right with interactive elements. Finally, the Feedback & Learning Agent captures teller feedback and resolution outcomes to continuously refine search algorithms and improve overall system performance using reinforcement learning techniques to adapt to new conversation patterns and emerging knowledge base updates.

Benefits & Capabilities of the Agents

• Modular AI Agent Framework for Enhanced Scalability: Each core teller support function conversation capture, query analysis, knowledge retrieval, contextual ranking, and feedback learning is handled by a dedicated, independently deployable agent. This modular architecture enables scalable deployment, targeted improvements, and seamless integration with existing banking systems, internal knowledge bases, or RegTech tools.

• End-to-End Conversation Intelligence & Information Retrieval: The system delivers complete process coverage from capturing live interactions and analyzing queries to retrieving and ranking relevant information. This ensures faster, more consistent responses with minimal manual intervention, transforming the traditional teller support workflow into an intelligent, responsive system.

• Real-Time Search & Contextual Display with Audit Trail: The Teller Assistance Agent instantly surfaces the most relevant documents and reference materials based on conversation context while maintaining a tamper-evident audit trail. Every agent action and decision is logged, enabling banks to review interactions, optimize agent performance, and maintain regulatory compliance with full visibility.

• Continuous Learning & Adaptive Improvement: By capturing teller feedback and transaction outcomes, the system continuously refines its models to improve accuracy, reduce false positives, and adapt to evolving customer queries and banking protocols. This ensures the system becomes more intelligent and effective over time, delivering increasingly personalized and accurate support.

Tech Stack Used