Customer support · Production

Parameta accelerates client email resolution with Amazon Bedrock Flows

The problem

Parameta's support team manually processed a growing volume of client emails — reading, classifying, extracting data, routing, and verifying in databases — a labor-intensive process that risked human error and produced inconsistent response quality.

First attempt

Traditional NLP pipelines relied on rigid rules and struggled with language variation across clients, while ML classification models required separate specialized models for each subtask with their own training data and contextual limitations.

Workflow diagram · grounded in source
1
Client email arrives via Teams
trigger
“When a client email arrives through Microsoft Teams, the workflow invokes the following stages”
2
Text extraction and S3 storage
integration
“using an AWS Lambda function to extract the text contained in the email and store it in Amazon Simple Storage Service (Amazon S3)”
3
Email classification
ai_action
“The classification prompt identifies the type of technical inquiry”
4
Entity extraction
ai_action
“The entity extraction prompt discovers key data points”
5
Completeness validation
validation
“The validation prompt verifies completeness of required information”
6
Agent queries knowledge base and data
ai_action
“An Amazon Bedrock agent synthesizes information from multiple sources: A custom knowledge base for technical documentation indexed in Amazon OpenSearch Service for relevant case history Enterprise data through Snowflake and Amazon Athena”
7
Response generation and delivery
output
“Response generation adapts based on validation results: Specific information requests for incomplete queries Comprehensive solutions for complete inquiries Delivery back to clients using Microsoft Teams”
Reported outcome

Parameta deployed an automated email triage system in two weeks, reducing client email resolution times and improving consistency, governance, and accuracy over the process.

Reported metrics
Client email resolution time (body)reduced from weeks to days
Client email resolution time (conclusion)reduced from days to minutes
Time to deploy solutiontwo weeks
Reported stack
Amazon Bedrock FlowsAmazon API GatewayAmazon Bedrock AgentsAmazon Bedrock Knowledge BasesAmazon OpenSearch ServiceSnowflakeAmazon AthenaMicrosoft Teams
Source
https://aws.amazon.com/blogs/machine-learning/parameta-accelerates-client-email-resolution-with-amazon-bedrock-flows?tag=soumet-20
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Parameta deployed an automated email triage system in two weeks, reducing client email resolution times and improving consistency, governance, and accuracy over the process.

What tools did this team use?

Amazon Bedrock Flows, Amazon API Gateway, Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon OpenSearch Service, Snowflake, Amazon Athena, Microsoft Teams.

What results were reported?

Client email resolution time (body): reduced from weeks to days; Client email resolution time (conclusion): reduced from days to minutes; Time to deploy solution: two weeks (source-reported, not independently verified).

What failed first in this deployment?

Traditional NLP pipelines relied on rigid rules and struggled with language variation across clients, while ML classification models required separate specialized models for each subtask with their own training data a…

How is this customer support AI workflow structured?

Client email arrives via Teams → Text extraction and S3 storage → Email classification → Entity extraction → Completeness validation → Agent queries knowledge base and data → Response generation and delivery.

Related customer support cases