NewDay builds a generative AI customer-service agent assist with over 90% accuracy
NewDay created a real-time generative AI assistant for contact center agents to answer customer questions from about 200 knowledge articles during live calls. The team used a serverless RAG design on AWS, iterated through eight experiment loops, improved accuracy from below 60% to over 90%, and cut answer retrieval time from 90 seconds to 4 seconds.
- Organization
- NewDay
- Industry
- Finance
- Location
- United Kingdom
- Published
- June 2025
Reported outcomes
+90%
accuracyQuality & accuracy
Strategic outcomes
Catalog median for quality & accuracy deployments: +90% across 270 reported metrics. Compare benchmarks →
Primary read
Use case focus
Showing 3 of 3
- 1Agent Assist
- 2Customer Service Automation
- 3RAG
- Agents needed faster, more accurate retrieval of answers from knowledge articles during live calls.
- The team had to manage cost, latency, limited infrastructure, and low confidence in the initial proof of concept.
- Built NewAssist as a real-time RAG chatbot/agent assist using Amazon Bedrock with Claude 3 Haiku.
- Used Amazon API Gateway, Amazon OpenSearch Serverless, AWS Fargate, and AWS Lambda in a serverless architecture.
- Created a golden dataset, custom data parsing for widget-based knowledge articles, and iterative evaluation gates before production.
- Accuracy improved from below 60% to over 90%.
- Answer retrieval time fell from about 90 seconds to 4 seconds.
- The solution rolled out to more than 150 agents and is planned for broader customer operations use.
- Running cost was kept under $400 per month.
Architecture
NewDay built NewAssist as a real-time retrieval-augmented generation assistant for contact center agents. The system uses Amazon API Gateway, Amazon OpenSearch Serverless, AWS Fargate, AWS Lambda, and Amazon Bedrock with Claude 3 Haiku. The team created a golden dataset, customized parsing for widget-based knowledge articles, and used weekly human review plus pre-production evaluation gates to improve accuracy before rollout.
Sources & evidence1
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