Case Study

Overcoming Manual Data Processing Inefficiencies to Achieve Automated AI-Powered Ingestion and Semantic Search for Autoom Studios

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Industry 

Technology and Data Processing

Expertise 

Amazon Bedrock, AWS Lambda, Amazon S3, Amazon API Gateway, Amazon DynamoDB

Offerings/solutions 

AI-powered data processing and contact management system with automated ingestion, vector embedding generation, and semantic search capabilities.

About the Client

Autoom Studios is a technology-driven organization focused on building intelligent data processing solutions. The company aims to simplify data handling, enhance search capabilities, and leverage AI to improve operational efficiency and decision-making through scalable cloud-native architectures.

Highlights

50%

Reduction in Operational Overhead

Within Minutes

Processing Thousands of Records

Enabled

Semantic Search and Intelligent Data Matching

The Challenge

The client struggled to efficiently process large volumes of structured data and manage contact information due to reliance on manual handling of CSV files and fragmented data sources. These limitations led to time-consuming workflows, increased risk of errors, poor scalability, and an inability to perform semantic search or derive meaningful insights from the data.

Solutions

  • Users upload CSV files via a web application, with Amazon API Gateway and AWS Lambda handling secure ingestion and validation.
  • Amazon Bedrock (Titan Text Embeddings v2) converts structured data and contacts into vector embeddings for semantic search.
  • Amazon S3 triggers AWS Lambda functions to process files row-by-row and generate embeddings efficiently through an event-driven workflow.
  • Google OAuth-based integration fetches user contacts, processes them, and converts them into searchable embeddings.
  • Amazon S3 stores raw files, Amazon Aurora PostgreSQL (pgvector) handles embedding storage and similarity search, and Amazon DynamoDB stores OAuth tokens and metadata.
  • AWS IAM, Amazon CloudWatch, and AWS CloudTrail ensure secure access, monitoring, and auditing.

The Results

Automated end-to-end data processing and contact ingestion, reducing operational overhead by over 50% while enabling semantic search and intelligent data matching through a scalable, serverless, event-driven architecture.

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