What challenges did Parameta Solutions face before AI implementation?

Parameta managed thousands of email-based client support requests with a manual process that required reading emails, extracting details, checking databases, and routing responses. The manual workflow slowed resolution, introduced human error risk, and made it difficult to maintain consistent response quality at scale.

What was the impact of the AI solution?

Parameta says the solution reduced email resolution time from weeks to days and minutes. The workflow improved accuracy, operational control, observability, governance, and the speed of prompt iteration. The implementation was completed in roughly two weeks, indicating rapid delivery of a production workflow.

Source

ExpandedScaled productionEvidence: Low40/100

Parameta accelerates client email resolution with Amazon Bedrock Flows

Use case typeCustomer communication automationUpdated Jun 13, 2026

Parameta Solutions, a provider of OTC data and analytics, used Amazon Bedrock Flows to transform a manual client email handling process into an automated workflow. The solution classifies incoming support emails, extracts entities, validates completeness, consults knowledge bases and enterprise data, and generates either full replies or requests for more information. The company reports that implementation took about two weeks and that the workflow improved governance, traceability, and iteration speed.

Organization: Parameta Solutions
Industry: Finance
Location: United Kingdom
Published: January 2025

Reported outcomes

Strategic outcomes

Speed & agilityAutomated email triage workflowRisk & complianceImproved governance and traceabilityNew product / capabilityGenerated full replies and follow-upsSpeed & agilityDelivered production workflow rapidly

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Parameta Solutions
Provider: AWS
Maturity: Scaled Production
Linked source: AWS Machine Learning Blog

The manual workflow slowed resolution, introduced human error risk, and made it difficult to maintain consistent response quality at scale

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1Email Triage
2Customer Support Automation
3Workflow Orchestration

Parameta managed thousands of email-based client support requests with a manual process that required reading emails, extracting details, checking databases, and routing responses.
The manual workflow slowed resolution, introduced human error risk, and made it difficult to maintain consistent response quality at scale.

Built an automated email triage workflow with Amazon Bedrock Flows as the central orchestrator.
Used specialized prompts for classification, entity extraction, validation, and response generation with deterministic branching and version management.
Connected the flow to Amazon S3, Amazon API Gateway, Amazon OpenSearch Service, Snowflake, and an Amazon Bedrock agent that synthesized responses from knowledge bases and enterprise data.

Technologies

Amazon Bedrock Flows Amazon Bedrock Agents Amazon Bedrock Knowledge Bases Amazon API Gateway Amazon S3 Amazon OpenSearch Service AWS Lambda Snowflake

Parameta says the solution reduced email resolution time from weeks to days and minutes.
The workflow improved accuracy, operational control, observability, governance, and the speed of prompt iteration.
The implementation was completed in roughly two weeks, indicating rapid delivery of a production workflow.

Architecture

Client emails enter the workflow through Amazon API Gateway and are stored in Amazon S3. Amazon Bedrock Flows orchestrates a sequence of prompts for classification, entity extraction, validation, and branching based on completeness. An Amazon Bedrock agent then synthesizes answers by consulting a technical knowledge base indexed in Amazon OpenSearch Service and enterprise data in Snowflake using Athena, and sends the response back through Microsoft Teams.

Sources & evidence1

Evidence: Low40/100Evidence strength