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Anomaly Detection
ChatLLM
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CHATLLM
A financial services firm
The company used Abacus.AI to identify key pieces of information from customer call transcripts and classify them
Problem
The company wants to reduce costs by automating the categorization of calls based on the transcripts
Solution
Used Abacus.AI Named Entity Recognition to automate this process
Results
Achieved 99% accuracy in classifying, saving multiple hours of human effort
Classified call records into 15 classes
Achieved accuracy of 99%
Aggregated over 100 datasets
CHATLLM
Leading Health Insurance Provider
Used Abacus.AI ChatLLM to quickly answer complex insurance claims related queries from customers
Problem
Company wanted to automate and improve accurate handling of claims
Solution
Created Abacus.AI chatLLM with context on hundreds of documents across tens of distinct problem areas to instantly answer customer specific queries on any health policy
Results
Reduced claims processing time by 90%
Extracted and cleaned text from thousands documents
Processed 1m+ claims received per week
Saved millions of dollars by automating and reducing claims processing time by 90%
CHATLLM
National Personal Injury Attorney
The company used Abacus.AI to identify key pieces of information from customer call transcripts and classify them
Problem
Used Abacus.AI to automate the creation of claimant-specific demand letters through AI agent
Solution
Company wanted to automate and streamline the labor intensive process of creating claimant demand letters, including data entry and state judicial document review
Results
Achieved 99% accuracy in classifying, saving multiple hours of human effort
All sensitive data secured safely
Ingested thousands of demand letters, depositions and positions to optimize output
Saved $1M+ by reducing demand document creation time by 60%
CHATLLM
Household Consumer Goods Organization
The company used Abacus.AI to streamline customer service operations by automating the analysis of call transcripts
Problem
The company was spending too much time and money manually categorizing call transcripts to improve customer service
Solution
Used Text Classification and Extraction solution to build a model that could analyze the call transcripts
Results
Increased workplace efficiency, nearly eliminating manual reviews
Automated transcript reviews with 99% accuracy
Aggregated data from 100+ datasets
Classified transcripts into 15 classes
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