call_center_ai · energy · workflow
Infosys Topaz uses Amazon Bedrock to cut technical help desk call handling time by 60%
A large energy supplier's technical help desk handled roughly 5,000 calls per week from meter technicians in the field, with average handling times exceeding 5 minutes for the top 10 issue categories—representing over 60% of call volume—and 60–70% of issues being repetitive. Scaling the support team was costly and not sustainable.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Meter technician calls help desk
Meter technicians call support agents from the technical help desk when they cannot fix issues on-site by themselves.
Tools used
Amazon BedrockAnthropic's Claude SonnetAWS Step FunctionsAmazon DynamoDBAmazon OpenSearch ServerlessAWS LambdaAmazon Titan Text EmbeddingsStreamlitPandasAWS Secrets Manager
Outcome
The AI assistant now handles 70% of previously human-managed calls, average handling time for the top 10 categories dropped from over 5 minutes to under 2 minutes (a 60% improvement), issues requiring human intervention fell from 30–40% to 20% within the first 6 months, and customer satisfaction scores increased by 30%.
Results
Time saved5,000 per week
Volumeover 60%
Grounding & classification
Source type: technical build writeup
46 fields verified against source quotes.
document classificationknowledge searchragsummarizationsupport agentcall recordingknowledge basefailure mode describedhuman review describedmetric backedproduction runtime claimedtools describedvendor confirmedworkflow describedenergyautomation ratecustomer satisfactioncycle time reductionemployee productivityresolution time reductiontechnical build writeupcall center aicustomer supportfield serviceit supportautonomous resolutionextract classify routerag answering