What challenges did Centras Group face before AI implementation?

Customer feedback was fragmented across multiple sources and languages, making manual analysis difficult. Scaling the original in-house model across new industries required retraining and manual tagging of tens of thousands of reviews per segment. Supporting multiple business segments simultaneously was operationally unsustainable.

What was the impact of the AI solution?

Manual review and data tagging work was reduced by up to 90 percent. The team moved from analyzing one business segment in about 2 days to more than 10 segments in the same time frame. The system consistently identifies more than 50 standardized issue categories. The solution is currently used internally across restaurants, banking apps, insurance, healthcare, and professional services.

Rank#1,303of 3,449

Source

Evidence: Medium50/100

Centras Group: AI-powered customer rankings using Amazon Bedrock and SageMaker

Use case typeCustomer experience analyticsUpdated Jun 13, 2026

Centras Group, a Kazakhstan-based diversified business firm spanning investment, insurance, healthcare, IT, restaurants, events, and education, built Centras Rankings to analyze public customer feedback across review sites, maps, and social channels. The platform uses AWS services to translate multilingual reviews, perform sentiment analysis, apply automated tagging, categorize feedback into standardized themes, and produce analytical outputs that feed Centras Rankings and the C500 framework.

Organization: Centras Group
Industry: Professional Services
Location: Kazakhstan
Published: May 2026

Reported outcomes

2 days

timeTime & speed

−90%quantified impact

Strategic outcomes

Speed & agilityExpanded analysis across more segmentsNew product / capabilityStandardized multilingual review analysisCost efficiencyReduced manual tagging workloadScale & capacityUsed across multiple business segments

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Quantified impact: 90% decrease

AWS Customer StoryMay 27, 2026Customer storyInferred claimMedium evidence strength

Manual review and data tagging work was reduced by up to 90 percent.

Normalized claim

Time: 2 days increase

AWS Customer StoryMay 27, 2026Customer storyInferred claimMedium evidence strength

The team moved from analyzing one business segment in about 2 days to more than 10 segments in the same time frame.

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

Customer: Centras Group
Provider: AWS
Maturity: Unknown
Linked source: AWS Customer Story

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 4

1Customer Experience Analytics
2Sentiment Analysis
3Text Classification

Customer feedback was fragmented across multiple sources and languages, making manual analysis difficult.
Scaling the original in-house model across new industries required retraining and manual tagging of tens of thousands of reviews per segment.
Supporting multiple business segments simultaneously was operationally unsustainable.

Centras uploads public customer reviews to Amazon S3.
Amazon SageMaker AI cleans and filters the data and prepares it for analysis.
Amazon Bedrock with Amazon Nova Pro translates between Kazakh and Russian, performs sentiment analysis, applies automated tagging, and categorizes feedback into more than 50 standardized themes.
Amazon SageMaker AI calculates performance metrics and prepares analytical outputs for internal use and for the Centras C500 framework.

Technologies

Amazon Bedrock Amazon Nova Pro Amazon SageMaker AI Amazon S3

Manual review and data tagging work was reduced by up to 90 percent.
The team moved from analyzing one business segment in about 2 days to more than 10 segments in the same time frame.
The system consistently identifies more than 50 standardized issue categories.
The solution is currently used internally across restaurants, banking apps, insurance, healthcare, and professional services.

Architecture

Centras uploads public customer reviews from maps, review sites, and social channels to Amazon S3. Amazon SageMaker AI performs data cleaning and filtering. Amazon Bedrock with Amazon Nova Pro translates between Kazakh and Russian, performs sentiment analysis, applies automated tagging, and categorizes feedback into standardized themes. Processed results are stored back in Amazon S3, and SageMaker AI calculates performance metrics that feed Centras Rankings and the C500 framework.

Sources & evidence1

Evidence: Medium50/100Evidence strength