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CogniBlend
Problem Statement
Delayed Access to Information
Insurance agents or Core banking firms often face delays in retrieving relevant customer information during calls, leading to inefficient service.
High Cognitive Load on Agents
Agents are frequently overwhelmed by the need to process and interpret complex information quickly, which can lead to errors. This pressure contributes to 74% of agents burning out.
Limited Real-Time Analytical Capabilities
Traditional systems lack the capability to analyze customer data and interaction context in real time, reducing the effectiveness of customer interactions.
Our Approach
Data Collection
We collect and integrate data from various sources like insurance and banking companies, human conversations etc. to create a thorough database for training.
Feature Engineering
Extract relevant features like policy number, names, dates etc. through Named Entity Recognition (NER) to understand queries.
Model Selection
Our product leverages powerful context understanding and GenAI models like large language models.
Training Techniques
Prompt engineering and fine-tuning improves the LLM performance by understanding context and target words.
Validation and Testing
Assess the model's performance across different subsets of the data. Continuously monitor the model's performance in live customer service environments.
Our Solution
- Our AI-powered solution analyzes queries and provides instant access to customer data, streamlining the response process.
- Alleviate the burden on agents by analyzing conversations and summarizing key points, allowing agents to focus on delivering clear and precise responses.
- Provide real-time analysis and insights, improving the relevance and quality of responses.
Impact
- Up to 50% reduction of call duration from an average of 17mins to 7 mins.
- With immediate access to detailed customer information, the AI assistant can enable a 30% reduction in average response times during customer interactions.
- Our solution ensures consistent information delivery, resulting in a significant reduction in customer complaints related to discrepancies.
- By alleviating the cognitive load on agents, our solution has increased agent productivity.
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