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Enterprise Data Analytics & Generative AI · Success story

Turning thousands of daily support tickets into instant answers

An AI-powered AWS platform transformed multilingual support tickets into structured, severity-scored data that teams can explore in natural language.

Datum Analysis logoClientDatum Analysis
~99%faster time to insight
15×more inquiries handled
100%multilingual tickets made usable
~50%lower AI processing cost

Selected AWS services

Core services in the architecture.

A focused view of the AWS services most representative of this engagement. The complete technical scope is described below.

  1. 01Amazon Bedrock
  2. 02AWS Lambda
  3. 03Amazon S3
  4. 04Amazon OpenSearch Service
  5. 05AWS Step Functions
  6. 06Amazon CloudWatch

Client story

Watch the project story.

Hear the context, approach, and results directly in this project feature.

01

The challenge

Datum Analysis’s clients generated thousands of Arabic and English support tickets each day. The raw data could not feed standard reporting, triage relied on manual judgment, and discovering a trend required days of hand-written queries.

02

What CloudVests delivered

CloudVests built an AI intelligence platform on AWS that cleans and structures incoming tickets, assigns severity and risk scores, and lets teams ask plain-language questions against the resulting evidence.

Incoming records were normalized through event-driven Lambda functions and retained in Amazon S3 with traceable source context. Step Functions coordinated enrichment, classification, severity scoring, and exception handling as an observable workflow.

Amazon Bedrock supported multilingual reasoning and structured extraction, while Amazon OpenSearch Service provided governed retrieval across the resulting evidence. CloudWatch captured latency, failures, and model-workflow behavior for operational review.

03

The outcome

The organization moved from manual, query-heavy investigation to accessible, near-instant operational insight while expanding the volume the team could handle.

The design separated source ingestion, model inference, and retrieval so each stage could be measured, scaled, and improved independently rather than operating as an opaque AI pipeline.