Case studies

Sedmi Odjel Builds AI-Powered Content Aggregation Platform on AWS

Sedmi odjel delivered a generative AI solution enabling automated content collection, summarization, and classification from diverse digital sources.

Intro

A digital products company focused on strategic innovation faced challenges efficiently monitoring and processing information from multiple digital channels. Manual content aggregation from newsletters, websites, and social media was time-consuming and couldn't scale with proliferating sources. Sedmi odjel delivered a generative AI solution leveraging Amazon Bedrock and a serverless architecture to automate the entire content intelligence pipeline—from collection through summarization and classification—reducing time-to-insight by over 90%.

The Problem

The customer's content monitoring process faced several critical limitations:

  • Limited Coverage: Manual monitoring could only process 50-100 content sources daily, missing important information across the expanding digital landscape
  • Delayed Insights: 24-48 hour delays between content publication and actionable insight availability impacted decision-making speed
  • Inconsistent Classification: Manual categorization led to inconsistent routing and missed or misclassified information
  • Resource Constraints: Content monitoring diverted skilled resources from higher-value strategic activities
  • Scalability Barriers: Adding new sources required proportional increases in manual effort

The company needed an automated solution capable of collecting content at scale, distilling it into actionable summaries, and accurately classifying information for downstream use.

Why AWS Cloud?

AWS emerged as the ideal platform for this content intelligence solution due to several key factors:

Foundation Model Access: Amazon Bedrock provides access to Anthropic Claude's 100K+ token context window, essential for processing lengthy articles and comprehensive summarization.

Serverless Scalability: ECS Fargate and Aurora Serverless automatically scale with content volume, handling variable workloads without manual intervention.

Integrated Services: Native integration between S3, Textract, Bedrock, and Aurora enables seamless data flow through the processing pipeline.

Multi-AZ Reliability: Managed services provide high availability across availability zones, ensuring consistent content monitoring.

Cost Optimization: Serverless pricing aligns costs with actual processing volume, providing predictable unit economics.

Solution

We implemented a production-ready content intelligence platform utilizing AWS managed services optimized for high-volume content processing and AI analysis.

Content Collection Layer

  • ECS Fargate containers for scalable web scraping and content collection
  • Specialized proxy integration for reliable access to diverse content sources
  • Support for newsletters, websites, and social media platforms

Processing & Storage

  • Amazon S3 for raw content storage with event-driven processing triggers
  • Amazon Textract for document text extraction
  • Amazon Aurora Serverless (PostgreSQL) for structured data persistence

Intelligence Layer

  • Amazon Bedrock with Anthropic Claude for content summarization
  • AI-powered classification for automated content categorization
  • Configurable classification taxonomies aligned with business requirements

Infrastructure & Networking

  • Multi-AZ deployment across Fargate, Aurora, and supporting services
  • Amazon CloudFront for content delivery
  • Amazon Route 53 for DNS management
  • VPC with private subnets for secure processing

CI/CD & Operations

  • AWS CodePipeline and CodeDeploy for automated deployments
  • Amazon ECR for container image management
  • AWS KMS and ACM for encryption and certificate management

The solution implements a three-stage AI pipeline: automated collection from diverse sources, intelligent summarization using Claude to distill high-volume content, and AI-powered classification to categorize information for actionable use.

Next Steps

The successful deployment has transformed content intelligence operations and established a foundation for continued enhancement:

Classification Refinement: Ongoing prompt optimization to improve classification accuracy for edge cases and emerging content categories.

Source Expansion: Integration of additional content sources and platforms based on customer requirements.

Advanced Analytics: Development of trend detection and anomaly identification capabilities across aggregated content.

Custom Alerting: Implementation of configurable alerts for high-priority content matching specific criteria.

Sedmi odjel continues to partner with the customer on expanding and optimizing their AI-powered content intelligence platform, enabling them to maintain competitive advantage through comprehensive, timely market awareness.

Results and Benefits
10x Content Coverage
Processing capacity increased from 50-100 to over 1,000 sources daily, dramatically expanding monitoring breadth.
90%+ Faster Insights
Time-to-insight reduced from 24-48 hours to under 2 hours, enabling near-real-time intelligence delivery.
Consistent Classification
AI-powered categorization ensures reliable, consistent routing of content to appropriate workflows.
Resource Optimization
Automated pipeline frees skilled resources for strategic analysis rather than manual content processing.
Scalable Architecture
Serverless infrastructure automatically handles volume fluctuations without manual scaling.
Production Deployment
Solution is fully deployed, actively used, and was featured at AWS User Group Zagreb Meetup as a successful implementation.
Cost-Efficient Operations
Monthly AWS costs of approximately $560 demonstrate efficient resource utilization at estimated ARR of $6,728 USD.
Ready to Build on AWS?

Let’s turn your ideas into high-performing AWS solutions. Reach out to us and let's start building!

Let’s talk

Other case studies