Case Study

UpContent Modernizes Search Infrastructure with AWS OpenSearch and Bedrock for AI-Ready Content Intelligence

At a glance

UpContent partnered with New Math Data to modernize its search infrastructure on AWS, delivering a fully automated, cloud-native OpenSearch ecosystem with hybrid search and Infrastructure as Code automation. Over an eight-week engagement, New Math Data is delivering a production-ready OpenSearch ecosystem that transforms UpContent’s search capabilities and establishes a modern foundation for AI-powered content intelligence.

Industry

Use Case

Migration & Implementation, Intelligent Document Processing, Compliance & Regulatory Management, Natural Language Processing

Solution implemented

The value equation

Company Snapshot

UpContent is a content-intelligence platform that helps businesses discover, curate, and share relevant content across multiple channels and industries.

Location

United States

Customer Situation

As UpContent’s customer base and query volumes grew, the engineering team identified an opportunity to evolve the search architecture to support the next phase of scaling. The engineering team was looking to head off concerns around platform scaling related to indexes, latency, scalability, and operational costs before it became a problem.

The team established architectural requirements: deliver faster and more relevant results for both text and vector queries, scale efficiently without manual maintenance, simplify deployment and reduce infrastructure drift, strengthen security and compliance posture, and provide a foundation for future AI-powered features.

NMD Solution

New Math Data collaborated with UpContent’s engineering team to design and deploy a cloud-native OpenSearch solution on AWS, enabling hybrid search, simplifying operations through automation, and hardening the environment against downtime and drift.

New Math Data engineered a highly available OpenSearch cluster across multiple Availability Zones with encrypted storage, optimized shard configurations with replica protection, and automated index lifecycle management.

NMD implemented hybrid search combining full-text keyword retrieval with vector embeddings from Amazon Bedrock Titan v2. The decision to consolidate vector search within OpenSearch represented a strategic architectural choice that reduced operational complexity, improved performance and latency, and better aligned with UpContent’s scale and compliance requirements.

All infrastructure was codified with Infrastructure as Code tooling, using remote state management for locking and drift detection. Automated secret rotation secured credentials across all data stores and services.

A three-tier network model was implemented with distinct zones for public API access, protected application logic, and private data storage. Web application firewalls protect ingress traffic, containerized services run application logic within the protected tier, and data stores are restricted to private access.

Logs from all services aggregate into centralized monitoring for cross-service queries. Automated alerts trigger on latency, storage, or error anomalies.

What We're Delivering

Over an eight-week engagement, New Math Data delivered a production-ready OpenSearch ecosystem that transforms UpContent’s search capabilities, reducing operational friction, enhancing security, and establishing a modern foundation for AI-powered content intelligence.

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